CN110246224A

CN110246224A - The surface denoising method and system of grid model

Info

Publication number: CN110246224A
Application number: CN201810190139.5A
Authority: CN
Inventors: 沈伯伟
Original assignee: Beijing Jingdong Century Trading Co Ltd; Beijing Jingdong Shangke Information Technology Co Ltd
Current assignee: Beijing Jingdong Century Trading Co Ltd; Beijing Jingdong Shangke Information Technology Co Ltd
Priority date: 2018-03-08
Filing date: 2018-03-08
Publication date: 2019-09-17
Anticipated expiration: 2038-03-08
Also published as: CN110246224B

Abstract

Present disclose provides a kind of surface denoising method of grid model, this method includes the first grid model for obtaining f frame image to be denoised；Obtain the grid model with the image that f frame image is successive frame, wherein include f-1 frame image and/or f+1 frame image with f frame image for the image of successive frame；And based on the grid model with the image that f frame image is successive frame, surface denoising is carried out to the first grid model.The disclosure additionally provides a kind of surface denoising system of grid model, a kind of computer system and a kind of computer readable storage medium.

Description

The surface denoising method and system of grid model

Technical field

This disclosure relates to field of computer technology, a kind of surface denoising method more particularly, to grid model and it is System, a kind of computer system and a kind of computer readable storage medium.

Background technique

The surface denoising of grid model is the classical problem of computer graphics, is widely used in Film Animation, electronics The fields such as game, virtual reality.Algorithm for reconstructing is still either used using scanning device, in the grid model finally obtained All inevitably noise and abnormal data, it is the one of current research that denoising is carried out on the basis of keeping grid search-engine A difficult point.Because, theoretically can not be to grid mould to the feature of grid model there is no specific definition in graphics Noise and grid search-engine in type are efficiently differentiated.There are when much noise especially in grid model, just more Noise data and characteristic information are accurately analyzed and distinguished to hardly possible, is especially difficult to differentiate between noise and sharp feature.

Currently, this problem is denoised for the surface of grid model, has had already appeared some processing methods.Wherein compare through The method of allusion quotation includes Laplce's smoothing method, the mesh denoising method based on bilateral filtering, the filtering of grid surface normal vector and top Point position method for reconstructing etc..

Wherein, for Laplce's smoothing method, the geometric meaning of Laplace operator is certain in grid model The weighted sum on all vertex in vertex and its neighborhood.The the length of Laplce's vector on vertex the big, indicates the apex more not Smoothly, length is smaller, indicates that the apex is more smooth.If the length of Laplce's vector on vertex is 0, then it represents that the vertex Just it has fallen in its neighborhood in the weighted center of gravity on all vertex.Exactly because this geometric properties of Laplace operator, very The surface denoising method of more grid models can make the position of grid vertex as far as possible close to the position of centre of gravity on its neighborhood vertex, so that The length of Laplce's vector is close to 0, to realize the purpose of the surface noise of removal grid model.

In addition, the mesh denoising method based on bilateral filtering is also a kind of surface denoising side of relatively conventional grid model Method, essence are a kind of popularizing forms of gaussian filtering.

However, the above method is all directly to be filtered denoising to the vertex in grid model.Due to grid model sheet Body inevitably includes noise, and the location information on each vertex inherently receives the interference of noise, so as to cause denoising It is ineffective.In order to solve this problem, occur first passing through in the prior art being filtered grid normal vector, further according to filter Normal vector after wave redefines the denoising method of vertex position.Such as it provides in the related technology a kind of while considering grid The face normal vector of model and the filtering method of point normal vector realize more preferable relative to the filtering method of only one normal vector of consideration Denoising effect.Simultaneously additionally provide a kind of grid normal vector denoising method with directive significance in the related technology, by with After other several surface denoising methods for having feature retention properties compare, discovery also may be implemented preferably to denoise effect Fruit.

Also, in order to retain the sharp noise in grid model, additionally provide in the prior art a kind of based on L₀Norm is most The mesh denoising method of smallization.Sparse Problems have been introduced into the surface denoising of grid model by this method, reference is also made to simultaneously The optimization method of two dimensional image finally realizes the L of grid model₀Norm minimum denoising.

Although the L of grid model₀Norm minimum denoising method has certain place for the model comprising random noise Manage effect, but if in grid model simultaneously also include it is abnormal raised, would become hard to remove these exception using this method Protrusion.Fig. 1 a~Fig. 1 d is diagrammatically illustrated using mesh denoising method in the related technology to the grid mould comprising random noise Type carries out the effect contrast figure before and after noise processed.As shown in Fig. 1 a~Fig. 1 d, wherein Fig. 1 a is diagrammatically illustrated comprising noise With abnormal raised original manikin, Fig. 1 c is diagrammatically illustrated comprising noise and abnormal raised primitive man's volume mesh model The raised detail view of middle exception, Fig. 1 b are diagrammatically illustrated using L₀The mesh denoising method of norm minimum is to the original human body Result figure after model denoising, Fig. 1 d diagrammatically illustrate the detail view of abnormal raised position in denoising descendant's volume mesh model, The part that box marks in Fig. 1 c and Fig. 1 d figure is raised for the exception for including in manikin.As can be seen from the figure model surface Most of random noise through L₀It is smoothened after the mesh denoising of norm minimum, but the mesa-shaped on surface is extremely raised But it does not eliminate.

During realizing disclosure design, at least there are the following problems in the related technology for inventor's discovery:

The effect denoised using mesh denoising method in the related technology to grid model surface is poor, it is difficult to meet and use Denoising requirement of the family to image procossing.

Summary of the invention

In view of this, present disclose provides the surface denoising method and system of a kind of grid model, a kind of computer system With a kind of computer readable storage medium.

An aspect of this disclosure provides a kind of surface denoising method of grid model, including obtaining f to be denoised First grid model of frame image；Obtain with above-mentioned f frame image be successive frame image grid model, wherein it is above-mentioned with Above-mentioned f frame image is that the image of successive frame includes f-1 frame image and/or f+1 frame image；And based on it is above-mentioned with it is above-mentioned F frame image is the grid model of the image of successive frame, carries out surface denoising to above-mentioned first grid model.

In accordance with an embodiment of the present disclosure, obtaining with the grid model for the image that above-mentioned f frame image is successive frame includes obtaining Take the second grid model of above-mentioned f-1 frame image and the third grid model of above-mentioned f+1 frame image；And to above-mentioned first It includes first to above-mentioned second grid model, above-mentioned third grid model and above-mentioned first net that grid model, which carries out surface denoising, Lattice model is normalized, by above-mentioned second grid model, above-mentioned third grid model and above-mentioned first grid model The model under the same coordinate system is converted to, then carries out surface denoising.

In accordance with an embodiment of the present disclosure, right based on the above-mentioned grid model with the image that above-mentioned f frame image is successive frame Above-mentioned first grid model carry out surface denoising include based on normalization after above-mentioned second grid model and above-mentioned third Grid model carries out surface denoising to above-mentioned first grid model after normalization, wherein to above-mentioned the after normalization One grid model carry out surface denoising include determine in above-mentioned second grid model and above-mentioned third grid model with it is above-mentioned The corresponding corresponding points in each vertex in first grid model；By vertex and the corresponding corresponding points in above-mentioned each vertex The distance between meet the corresponding points of preset threshold and be determined as match point corresponding with the vertex；And it is upper according to what is determined The match point in the match point and above-mentioned third grid model in the second grid model is stated according to predetermined denoising model to above-mentioned One grid model carries out surface denoising.

In accordance with an embodiment of the present disclosure, the surface denoising method of above-mentioned grid model further include vertex with it is corresponding right It the distance between should put in the case where being unsatisfactory for above-mentioned preset threshold, regard the vertex itself as the corresponding matching in the vertex Point；And surface denoising is carried out to above-mentioned first grid model according to above-mentioned predetermined denoising model according to the vertex itself.

In accordance with an embodiment of the present disclosure, above-mentioned predetermined denoising model includes being based on L₀The successive frame grid of norm minimum is gone It makes an uproar model, wherein above-mentioned to be based on L₀Above-mentioned second grid mould is included at least in the successive frame mesh denoising model of norm minimum Type and above-mentioned third grid model bound term, the above method further include determining above-mentioned second grid model and above-mentioned third grid mould The constraint weight of type bound term；And according to above-mentioned constraint weight according to above-mentioned predetermined denoising model to above-mentioned first grid model Carry out surface denoising.

In accordance with an embodiment of the present disclosure, the surface denoising method of above-mentioned grid model further includes obtaining above-mentioned second grid mould Second colouring information of the first colouring information of the match point in type and the match point in above-mentioned third grid model；According to above-mentioned First colouring information determines the first color weight of the match point in above-mentioned second grid model, and is believed according to above-mentioned second color Breath determines the second color weight of the match point in above-mentioned third grid model；And according to above-mentioned first color weight, above-mentioned Second color weight and above-mentioned constraint weight and according to above-mentioned predetermined denoising model to above-mentioned first grid model after normalization Carry out surface denoising.

Another aspect of the disclosure provides a kind of surface denoising system of grid model, including first obtain module, Second obtains module and first processing module.First acquisition module is used to obtain the first grid mould of f frame image to be denoised Type；Second acquisition module be used for obtains with above-mentioned f frame image for successive frame image grid model, wherein it is above-mentioned with it is upper Stating the image that f frame image is successive frame includes f-1 frame image and/or f+1 frame image；And first processing module is used for Based on the above-mentioned grid model with the image that above-mentioned f frame image is successive frame, surface is carried out to above-mentioned first grid model and is gone It makes an uproar processing.

In accordance with an embodiment of the present disclosure, above-mentioned second acquisition module is used to obtain the second grid of above-mentioned f-1 frame image The third grid model of model and above-mentioned f+1 frame image；And above-mentioned first processing module is used for first to above-mentioned second grid Model, above-mentioned third grid model and above-mentioned first grid model are normalized, by above-mentioned second grid model, on It states third grid model and above-mentioned first grid model is converted to model under the same coordinate system, then carry out surface denoising.

In accordance with an embodiment of the present disclosure, above-mentioned first processing module is used for based on above-mentioned second grid model after normalization With above-mentioned third grid model, surface denoising is carried out to above-mentioned first grid model after normalization, wherein above-mentioned first Processing module includes the first determination unit, the second determination unit and first processing units.First determination unit is above-mentioned for determining Corresponding points corresponding with each vertex in above-mentioned first grid model in second grid model and above-mentioned third grid model； Second determination unit is used to the distance between vertex and the corresponding corresponding points in above-mentioned each vertex meeting preset threshold Corresponding points be determined as match point corresponding with the vertex；And first processing units are used for according to above-mentioned second determined The match point in match point and above-mentioned third grid model in grid model is according to predetermined denoising model to above-mentioned first grid Model carries out surface denoising.

In accordance with an embodiment of the present disclosure, above-mentioned first processing module further includes third determination unit and the second processing unit. It, will in the case that third determination unit is used to be unsatisfactory for above-mentioned preset threshold at a distance from vertex is between corresponding corresponding points The vertex itself is used as the corresponding match point in the vertex；And the second processing unit is used for according to the vertex itself according to above-mentioned Predetermined denoising model carries out surface denoising to above-mentioned first grid model.

In accordance with an embodiment of the present disclosure, above-mentioned predetermined denoising model includes being based on L₀The successive frame grid of norm minimum is gone It makes an uproar model, wherein above-mentioned to be based on L₀Above-mentioned second grid mould is included at least in the successive frame mesh denoising model of norm minimum Type and above-mentioned third grid model bound term, above system further include the first determining module and Second processing module.First determines Module is used to determine the constraint weight of above-mentioned second grid model and above-mentioned third grid model bound term；And second processing mould Block is used to carry out surface denoising to above-mentioned first grid model according to above-mentioned predetermined denoising model according to above-mentioned constraint weight.

In accordance with an embodiment of the present disclosure, above system further includes that third obtains module, the second determining module and third processing Module.Third obtains the first colouring information and above-mentioned third net that module is used to obtain the match point in above-mentioned second grid model Second colouring information of the match point in lattice model；Second determining module is used to determine above-mentioned the according to above-mentioned first colouring information First color weight of the match point in two grid models, and above-mentioned third grid model is determined according to above-mentioned second colouring information In match point the second color weight；And third processing module is used for according to above-mentioned first color weight, above-mentioned second face Color weight and above-mentioned constraint weight simultaneously carry out table to above-mentioned first grid model after normalization according to above-mentioned predetermined denoising model Face denoising.

Another aspect of the disclosure provides a kind of computer system, including one or more processors；Memory is used In the one or more programs of storage, wherein when said one or multiple programs are executed by said one or multiple processors, make It obtains said one or multiple processors realizes the surface denoising method of grid model as described above.

Another aspect of the disclosure provides a kind of computer readable storage medium, is stored thereon with executable instruction, The instruction makes processor realize the surface denoising method of grid model as described above when being executed by processor.

Another aspect of the present disclosure provides a kind of computer program, and the computer program, which includes that computer is executable, to be referred to It enables, described instruction is when executed for realizing the surface denoising method of grid model as described above.

In accordance with an embodiment of the present disclosure, because use based on f frame image for the grid model of the image of successive frame, The technological means that surface denoising is carried out to the first grid model increases when carrying out surface denoising to the first grid model The constraint of the grid model of the image of consecutive frame provides the limit of optimization grid model for the denoising of present frame grid model System.It is deposited when being denoised in the prior art according only to the grid model itself wait denoise frame image so at least partially overcoming Noise and abnormal raised can not be being clearlyed distinguish in grid model, the technology of grid model surface denoising effect difference is caused to be asked Topic, and then reached and met the technical effect that user requires the denoising of image procossing.

Detailed description of the invention

By referring to the drawings to the description of the embodiment of the present disclosure, the above-mentioned and other purposes of the disclosure, feature and Advantage will be apparent from, in the accompanying drawings:

Fig. 1 a~Fig. 1 d is diagrammatically illustrated using mesh denoising method in the related technology to the net comprising random noise Lattice model carries out the effect contrast figure before and after noise processed；

Fig. 2 diagrammatically illustrates the surface denoising method and system that can apply grid model according to the embodiment of the present disclosure Exemplary system architecture；

Fig. 3 diagrammatically illustrates the flow chart of the surface denoising method according to the grid model of the embodiment of the present disclosure；

Fig. 4 diagrammatically illustrates the flow chart of the surface denoising method of the grid model according to another embodiment of the disclosure；

Fig. 5 a~5f diagrammatically illustrates the effect for adding predetermined threshold in predetermined denoising model according to the embodiment of the present disclosure Fruit schematic diagram；

Fig. 5 g diagrammatically illustrates the effect diagram of denoising optimization when not considering successive frame according to the embodiment of the present disclosure.

Fig. 6, which is diagrammatically illustrated, is defined on side p₁p₃The schematic diagram based on area operator；

Fig. 7 diagrammatically illustrates the flow chart of the surface denoising method of the grid model according to another embodiment of the disclosure；

Fig. 8 diagrammatically illustrates the flow chart of the surface denoising method of the grid model according to another embodiment of the disclosure；

Fig. 9 a diagrammatically illustrates the schematic diagram of the original mesh model comprising color according to the embodiment of the present disclosure；

Fig. 9 b diagrammatically illustrates the schematic diagram of the original mesh model after the removal color according to the embodiment of the present disclosure；

Figure 10 a~10c diagrammatically illustrates the second grid model of increase and third grid mould according to the embodiment of the present disclosure The result schematic diagram of denoising result after the constraint weight of type bound term；

Figure 10 d, which is diagrammatically illustrated, not to be considered successive frame constraint and individually uses L to present frame₀The grid of norm minimum Result schematic diagram after denoising method；

Figure 11 diagrammatically illustrates the flow chart of the surface denoising method of the grid model according to another embodiment of the disclosure；

Figure 12 a~12f is diagrammatically illustrated according to the embodiment of the present disclosure based on L₀The successive frame grid of norm minimum The Comparative result schematic diagram of the denoising method processing mesa-shaped protrusion front and back of denoising model；

Figure 13 a~13h diagrammatically illustrates the L using single frame according to the embodiment of the present disclosure₀Norm minimum grid Denoising method and it is different constraint weight based on L₀The denoising method of the successive frame mesh denoising model of norm minimum is to grid The Comparative result schematic diagram of the raised denoising of the spherical exception of semicircle in model；

Figure 14 a~14h diagrammatically illustrates the L using single frame according to the embodiment of the present disclosure₀Minimize mesh denoising Method and it is different constraint weight based on L₀The denoising method of the successive frame mesh denoising model of norm minimum is to sharp in model The Comparative result schematic diagram of the abnormal raised denoising in end；

Figure 15 diagrammatically illustrates the block diagram of the surface denoising system according to the grid model of the embodiment of the present disclosure；

Figure 16 diagrammatically illustrates the block diagram of the first processing module according to the embodiment of the present disclosure；

Figure 17 diagrammatically illustrates the block diagram of the first processing module according to another embodiment of the disclosure；

Figure 18 diagrammatically illustrates the block diagram of the surface denoising system of the grid model according to another embodiment of the disclosure；

Figure 19 diagrammatically illustrates the block diagram of the surface denoising system of the grid model according to another embodiment of the disclosure；With And

Figure 20 diagrammatically illustrates the meter of the surface denoising method for being adapted for carrying out grid model according to the embodiment of the present disclosure The block diagram of calculation machine system.

Specific embodiment

Hereinafter, will be described with reference to the accompanying drawings embodiment of the disclosure.However, it should be understood that these descriptions are only exemplary , and it is not intended to limit the scope of the present disclosure.In addition, in the following description, descriptions of well-known structures and technologies are omitted, with Avoid unnecessarily obscuring the concept of the disclosure.

Term as used herein is not intended to limit the disclosure just for the sake of description specific embodiment.It uses herein The terms "include", "comprise" etc. show the presence of the feature, step, operation and/or component, but it is not excluded that in the presence of Or add other one or more features, step, operation or component.

There are all terms (including technical and scientific term) as used herein those skilled in the art to be generally understood Meaning, unless otherwise defined.It should be noted that term used herein should be interpreted that with consistent with the context of this specification Meaning, without that should be explained with idealization or excessively mechanical mode.

It, in general should be according to this using statement as " at least one in A, B and C etc. " is similar to Field technical staff is generally understood the meaning of the statement to make an explanation (for example, " system at least one in A, B and C " Should include but is not limited to individually with A, individually with B, individually with C, with A and B, with A and C, have B and C, and/or System etc. with A, B, C).Using statement as " at least one in A, B or C etc. " is similar to, generally come Saying be generally understood the meaning of the statement according to those skilled in the art to make an explanation (for example, " having in A, B or C at least One system " should include but is not limited to individually with A, individually with B, individually with C, with A and B, have A and C, have B and C, and/or the system with A, B, C etc.).It should also be understood by those skilled in the art that substantially arbitrarily indicating two or more The adversative conjunction and/or phrase of optional project shall be construed as either in specification, claims or attached drawing A possibility that giving including one of these projects, either one or two projects of these projects.For example, phrase " A or B " should A possibility that being understood to include " A " or " B " or " A and B ".

Embodiment of the disclosure provides the surface denoising method and system of a kind of grid model.This method include obtain to First grid model of the f frame image of denoising；Obtain the grid model with the image that f frame image is successive frame, wherein with F frame image is that the image of successive frame includes f-1 frame image and/or f+1 frame image；And based on being with f frame image The grid model of the image of successive frame carries out surface denoising to the first grid model.

Fig. 2 diagrammatically illustrates the surface denoising method and system that can apply grid model according to the embodiment of the present disclosure Exemplary system architecture.It should be noted that only can showing using the system architecture of the embodiment of the present disclosure shown in Fig. 2 Example, to help skilled in the art to understand the technology contents of the disclosure, but is not meant to that the embodiment of the present disclosure cannot be used In other equipment, system, environment or scene.

As shown in Fig. 2, system architecture 100 may include terminal device 101,102,103, network according to this embodiment 104 and server 105.Network 104 between terminal device 101,102,103 and server 105 to provide communication link Medium.Network 104 may include various connection types, such as wired and or wireless communications link etc..

User can be used terminal device 101,102,103 and be interacted by network 104 with server 105, to transmit message Deng.Camera perhaps scanning means or terminal device 101,102,103 can be installed on terminal device 101,102,103 It is separately configured as scanning means etc..

Terminal device 101,102,103 can be various electronic equipments, including but not limited to smart phone, tablet computer, Pocket computer on knee and desktop computer, video camera, scanner etc..

Server 105 can be to provide the server of various services, such as utilize terminal device 101,102,103 to user The server (merely illustrative) of image procossing is provided after obtaining image.Server can divide the data such as the image received The processing such as analysis, and processing result is fed back into terminal device.

It should be noted that the surface denoising method of grid model provided by the embodiment of the present disclosure generally can be by servicing Device 105 executes.Correspondingly, the surface denoising system of grid model provided by the embodiment of the present disclosure generally can be set in service In device 105.The surface denoising method of grid model provided by the embodiment of the present disclosure can also be by being different from server 105 and energy Enough servers communicated with terminal device 101,102,103 and/or server 105 or server cluster execute.Correspondingly, this public affairs The surface denoising system for opening grid model provided by embodiment also can be set in being different from server 105 and can be with terminal In the server or server cluster that equipment 101,102,103 and/or server 105 communicate.Alternatively, the embodiment of the present disclosure is mentioned The surface denoising method of the grid model of confession can also be executed by terminal device 101,102 or 103, or can also be by difference It is executed in other terminal devices of terminal device 101,102 or 103.Correspondingly, grid model provided by the embodiment of the present disclosure Surface denoising system also can be set in terminal device 101,102 or 103, or be set to different from terminal device 101, In 102 or 103 other terminal devices.

For example, image to be denoised can be stored in originally in terminal device 101,102 or 103 any one (for example, Terminal device 101, but not limited to this) among, or be stored on External memory equipment and terminal device 101 can be imported into In.Then, terminal device 101 can be performed locally the surface denoising method of grid model provided by the embodiment of the present disclosure, Or other terminal devices, server or server cluster are sent by image to be denoised, and by receiving the image to be denoised Other terminal devices, server or server cluster denoise to execute the surface of grid model provided by the embodiment of the present disclosure Method.

It should be understood that the number and type of terminal device, network and server in Fig. 2 are only schematical.According to It realizes and needs, can have the terminal device, network and server of arbitrary number and type.

Fig. 3 diagrammatically illustrates the flow chart of the surface denoising method according to the grid model of the embodiment of the present disclosure.

As shown in figure 3, this method includes operation S201~S203, in which:

In operation S201, the first grid model of f frame image to be denoised is obtained.

In accordance with an embodiment of the present disclosure, more views can be carried out to iconic model by using scanning device or algorithm for reconstructing The Image Acquisition at angle, and the three-dimensional grid model of corresponding each frame image is reconstructed, the grid mould of f frame image can be obtained Type, wherein the type of f frame image may include a variety of, such as can be the human body image in movement, animal painting or quiet State image etc..

In operation S202, the grid model with the image that f frame image is successive frame is obtained, wherein be with f frame image The image of successive frame includes f-1 frame image and/or f+1 frame image.

In accordance with an embodiment of the present disclosure, f-1 frame image and f+1 frame image and f frame image are the image of successive frame, It has sequencing on the time for obtaining image, can be three images continuously acquired.

The first grid model is carried out based on the grid model with the image that f frame image is successive frame in operation S203 Surface denoising.

In accordance with an embodiment of the present disclosure, based on the grid model with the image that f frame image is successive frame, to the first grid Model progress surface denoising, which can be, includes the following three types situation, the first situation, the grid mould based on f-1 frame image Type carries out surface denoising to the first grid model；Second situation, based on the grid model of f+1 frame image, to first Grid model carries out surface denoising；The third situation, grid model and f-1 frame image based on f+1 frame image Grid model carries out surface denoising to the first grid model.

In the related technology, only consider that presence can not clearly distinguish grid in the grid model denoising method of single frame image Noise and exception are raised in model, and the effect for causing grid model surface to denoise is poor, it is difficult to meet user and go to image procossing It makes an uproar requirement.In accordance with an embodiment of the present disclosure, the image that consecutive frame is increased when the denoising of surface is carried out to the first grid model Grid model constraint, provide help for the denoising of present frame grid model.

In accordance with an embodiment of the present disclosure, obtaining with the grid model for the image that f frame image is successive frame includes obtaining the Second grid model of f-1 frame image and the third grid model of f+1 frame image；And surface is carried out to the first grid model Denoising includes that first the second grid model, third grid model and the first grid model are normalized, by the Two grid models, third grid model and the first grid model are converted to the model under the same coordinate system, then carry out surface denoising Processing.

In accordance with an embodiment of the present disclosure, when second of third grid model and f-1 frame image based on f+1 frame image All grid models in the case where carrying out surface denoising to the first grid model, can be carried out normalizing by grid model Change operation, so that the first grid model, the second grid model and third grid model are aligned as much as possible.

In accordance with an embodiment of the present disclosure, which can guarantee the grid model of each frame image Size be it is unified, and center and towards be in an identical coordinate system so as to the first grid model into It, can be in grid model that is more convenient and accurately searching out consecutive frame image with first during the denoising of row surface The corresponding match point in vertex in grid model.

Below with reference to Fig. 4, Fig. 7, Fig. 8 and Figure 11, method shown in Fig. 3 is described further in conjunction with specific embodiments.

Fig. 4 diagrammatically illustrates the flow chart of the surface denoising method of the grid model according to another embodiment of the disclosure.

Based on the grid model with the image that f frame image is successive frame, the first grid model is carried out at the denoising of surface Reason include based on normalization after the second grid model and third grid model, to after normalization the first grid model carry out table Face denoising, wherein as shown in figure 4, carrying out surface denoising to the first grid model after normalization includes operation S2031~S2033, in which:

Operation S2031, determine in the second grid model and third grid model with each top in the first grid model The corresponding corresponding points of point.

In accordance with an embodiment of the present disclosure, after obtaining the second grid model and third grid model, the can be calculated Point in two grid models and third grid model in the first grid model of distanceNearest point index, wherein f indicates f Frame image, the point index can be indicated with function NN (i), from the point index in can determine in the first grid model The corresponding corresponding points in each vertex.

In operation S2032, the distance between vertex and the corresponding corresponding points in each vertex are met into preset threshold Corresponding points be determined as match point corresponding with the vertex.

In accordance with an embodiment of the present disclosure, it can first judge each vertex in the first grid model and corresponding corresponding points The distance between whether meet preset threshold, meet at a distance from the vertex in each vertex is between corresponding corresponding points pre- If in the case where threshold value, which is determined as match point corresponding with the vertex.

In accordance with an embodiment of the present disclosure, the main function of preset threshold is to limit in the grid model of consecutive frame image Influence of the excessive point of positional distance to the grid model of current frame image, because excessive preset threshold may result in currently Deformation occurs for the motion parts of the grid model of frame image, and too small preset threshold may weaken the grid to current frame image The denoising effect of abnormal conditions present in model.

Specifically, Fig. 5 a~5f is diagrammatically illustrated adds predetermined threshold according to the embodiment of the present disclosure in predetermined denoising model The effect diagram of value.

As shown in Fig. 5 a~5f, the disclosure takes different threshold values to carry out reality respectively under different predetermined denoising models It tests, wherein Fig. 5 a and 5d illustrates the result of not set threshold optimization under Bu Tong predetermined denoising model, it can be seen that transported in human body Serious deformation significantly has occurred in arm and leg when dynamic.Fig. 5 b and Fig. 5 e is illustrated to be added under Bu Tong predetermined denoising model Add threshold value be 0.4 after carry out again grid model denoising as a result, relative to Fig. 5 a and 5d, the degree of deformation is reduced.Into one Step ground, can reduce threshold value to constrain the deformation of arm and leg, Fig. 5 c and 5f illustrates Bu Tong predetermined denoising model lower threshold value The result of model optimization when being 0.1, it can be seen that the abnormal deformation of arm and leg has become very little, it is virtually impossible to find out With the difference for not considering to denoise optimum results (Fig. 5 g) when consecutive frame.

Accordingly, it can be seen that the importance of preset threshold constraint is added in predetermined denoising model, it can by preset threshold With the problem of preventing the motive position of manikin after optimizing to be abnormal deformation.

It should be noted that can according to circumstances set the size of preset threshold in actual experiment or application, herein It repeats no more.

In operation S2033, according to the matching in the match point and third grid model in the second grid model determined Point carries out surface denoising to the first grid model according to predetermined denoising model.

In accordance with an embodiment of the present disclosure, predetermined denoising model can be based on L₀The successive frame mesh denoising of norm minimum Model, L₀Norm indicates the number of all nonzero elements in vector, in L₀Increase in the mesh denoising model of norm minimum adjacent The constraint of grid model in frame image, for removing the extraordinary noise for appearing in the grid model in individual frame images.For example, The predetermined denoising model may is that

Wherein, p^fThe vertex set of the first grid model after indicating denoising optimization, p^f*Indicate the top of the first grid model Point set, α, β, γ are weight coefficient.First item constrains in the grid model of denoising optimization front and back between corresponding vertex in above formula Distance.Section 2 is model regularization bound term, and Fig. 6, which is diagrammatically illustrated, is defined on side p₁p₃Showing based on area operator It is intended to.As shown in fig. 6, defining R (p)=p to make the triangle topological structure in the grid model after denoising as regular as possible₁- p₂+p₃-p₄。

Section 3 is the position constraint of consecutive frame grid model, whereinIn the first grid model for indicating f frame image Point, which, which can be, is come out by the f frame multi-angle image rebuilding that collects, F=2, 3 ..., N-1 }.Indicate the point in the third grid model of f+1 frame image,Indicate the second of f-1 frame image Point in grid model.

Indicate that the distance of adjacent continuous frame grid model matching double points present frame corresponding points will meet the preset threshold of setting T。

Section 4 D (p) is a vector, wherein being corresponded on i-th side of grid model comprising i-th of element based on area Operator.As shown in fig. 6, if p₁, p₂, p₃And p₄This four o'clock in a plane, then this vector and be 0.For net Any a line e on lattice model, the operator D (e) based on area being unfolded are as follows:

In order to solve to obtain the optimal solution of above-mentioned model, the disclosure adds an auxiliary variable δ (δ mono- in model (1) A vector), above formula becomes formula (4):

Wherein, λ is smoothing factor, for control optimization after grid model smoothness.Net after optimization can be made by increasing λ Lattice model surface is more smooth, but the minutia that excessive λ may result in model surface is lost, it is therefore desirable to according to need Carry out adjusting parameter λ.

It, can be in two steps respectively to variable p for solving model (4)^fIt is calculated with δ, first holding p^fIt immobilizes, Carry out optimized variable δ, which becomes:

Formula (5) is a hard -threshold problem, has globally optimal solution.When solving this minimization problem,For D (p^f) i-th of element) when, allow δ_i=0 (δ_iFor i-th of element of vector δ)；It is no Then δ_i=D (p^f)_i.After the completion of solving δ, in next step, fixed variable δ is constant, solves p^f, minimization problem becomes:

Minimization problem in formula (6) is secondary, therefore can find minimum value by derivation.Solving p^f Afterwards, an iteration is just completed, factor alpha ← μ is updated_αα and γ ← μ_yγ optimizes again through the above process, until γ reaches threshold Value γ_max, optimization process terminates.μ_αThe value less than 1 should be taken, to reduce the influence of constraint；And μ_γThe value greater than 1 should be taken, is come Allow D (p^f) approach δ.In accordance with an embodiment of the present disclosure, table 1 is L₀The successive frame grid model denoising method parameter of norm minimum Table.

Table 1

Wherein, it indicatesThe mean dihedral angle of current optimization frame initial mesh model measures radian, l_eIndicate current optimization frame The average side length of initial mesh model.I.e. the two parameters can indicate the degree of irregularity of archetype and respectively comprising noise Degree, with degree of irregularityIncrease, the factor alpha of R (p) item also will increase in disclosure model, to inhibit optimization As a result the triangle gridding degree of irregularity in.Similarly, in model include noise level l_eIncrease, it is corresponding in Optimized model L₀Term coefficient also will increase, to realize the denoising of more great dynamics.

In accordance with an embodiment of the present disclosure, when the grid model based on f-1 frame image, surface is carried out to the first grid model When denoising, predetermined denoising model be may is that

In accordance with an embodiment of the present disclosure, when the grid model based on f+1 frame image, surface is carried out to the first grid model When denoising, predetermined denoising model be may is that

Fig. 7 diagrammatically illustrates the flow chart of the surface denoising method of the grid model according to another embodiment of the disclosure.

As shown in fig. 7, carrying out surface denoising to the first grid model after normalization to further include including operation S2034 ~S2035, in which:

It, will in the case where being unsatisfactory for preset threshold at a distance from vertex is between corresponding corresponding points in operation S2034 The vertex itself is used as the corresponding match point in the vertex.

In operation S2035, surface denoising is carried out to the first grid model according to predetermined denoising model according to the vertex itself Processing.

In accordance with an embodiment of the present disclosure, when in the first grid model vertex with the distance between with corresponding corresponding points In the case where being unsatisfactory for preset threshold, the corresponding points that the vertex itself replaces the grid model of consecutive frame image, root can be used When carrying out surface denoising to the first grid model according to predetermined denoising model according to the vertex itself, closed in predetermined denoising model In consecutive frame grid model bound term be 0.

In accordance with an embodiment of the present disclosure, it regard the vertex itself in the first grid model as corresponding match point, it can be with Make the triangle topological structure in the network model after denoising as regular as possible.

Fig. 8 diagrammatically illustrates the flow chart of the surface denoising method of the grid model according to another embodiment of the disclosure.

According to the embodiment of this liter, predetermined denoising model includes being based on L₀The successive frame mesh denoising mould of norm minimum Type, wherein be based on L₀The second grid model and third grid are included at least in the successive frame mesh denoising model of norm minimum Model bound term.As shown in figure 8, the surface denoising method of grid model further includes operation S204~S205, in which:

In operation S204, the constraint weight of the second grid model and third grid model bound term is determined.

In operation S205, the first grid model is carried out at the denoising of surface according to predetermined denoising model according to constraint weight Reason.

In accordance with an embodiment of the present disclosure, the constraint weight such as formula of the second grid model and third grid model bound term (1) value of the β recorded in, β can be determined according to experiment or practical application, such as can be 0.1,0.4,0.5,0.8 With 1 etc. numerical value.

In accordance with an embodiment of the present disclosure, constraint weight beta can be substituted into predetermined denoising model, to the first grid model into The denoising of row surface.

In accordance with an embodiment of the present disclosure, by taking different constraint weight betas as an example, to the surface of the grid model using the disclosure The effect of denoising method processing grid model is illustrated.

Fig. 9 a diagrammatically illustrates the schematic diagram of the original mesh model comprising color according to the embodiment of the present disclosure.

Fig. 9 b diagrammatically illustrates the schematic diagram of the original mesh model after the removal color according to the embodiment of the present disclosure.

It as shown in figures 9 a and 9b, is from top to bottom the grid model of continuous three frames image.Wherein, Fig. 9 a is to include face The grid model of color information, Fig. 9 b are to eliminate colouring information, the grid after being added to the topological connection relation in grid model Model.It can be seen that this three frames grid model includes a large amount of random noise from Fig. 9 a and Fig. 9 b.

Figure 10 a~10c diagrammatically illustrates the second grid model of increase and third grid mould according to the embodiment of the present disclosure The result schematic diagram of denoising result after the constraint weight of type bound term.The disclosure has used different weight coefficients to be tested, The constraint weight beta that the second grid model and third grid model bound term are respectively shown in Figure 10 a~10c is 0.8,0.5, 0.1 denoising result.

Knot is denoised after increasing the constraint weight of the second grid model and third grid model bound term as we can see from the figure The influence of fruit, it is 0.8 that Figure 10 a, which illustrates setting constraint weight beta, it is that when not set distance threshold, optimizes as a result, discovery by There is more apparent deformation in three frame grid models in human arm part, so if throw the reins to, successive frame matching Obtained point can make present frame model arm segment that more serious deformation occur, to deformation feelings as shown in Figure 10 a occur Condition.Figure 10 b, which is illustrated, reduces the grid model optimized after constraint weight beta is 0.5, can see compared with result in Figure 10 a It arrives, with the reduction of weight coefficient, the deformation of arm and leg position is also reduced.Figure 10 c illustrates reduction constraint weight beta The grid model optimized after 0.1, hence it is evident that it can be found that relative to two optimum results, mould shown in Figure 10 a and Figure 10 b Deformation at type leg and arm significantly reduces.

Figure 10 d, which is diagrammatically illustrated, not to be considered successive frame constraint and individually uses L to present frame₀The grid of norm minimum Result schematic diagram after denoising method.The result shows that: with the increase of constraint weight beta, successive frame grid model is to present frame net The influence of lattice model becomes larger, and biggish deformation occurs in the relative motion part that will lead to present frame grid model.In order to avoid this Kind situation, it should appropriate to reduce constraint weight beta or be arranged what a suitable threshold value had an impact to constrain present frame model Relating dot.

In accordance with an embodiment of the present disclosure, in L₀Consecutive frame net is added on the basis of the mesh denoising method of norm minimum The constraint of lattice model corresponding position, with using the position relevance in consecutive phantom, the denoising for present frame grid model is provided More references, improve the denoising optimization ability of present frame grid model, have reached preferable effect of optimization.

Figure 11 diagrammatically illustrates the flow chart of the surface denoising method of the grid model according to another embodiment of the disclosure.

As shown in figure 11, the surface denoising method of grid model further includes operation S206~S208, in which:

In operation S206, the first colouring information and third grid model for obtaining the match point in the second grid model Second colouring information of match point.

In operation S207, the first color weight of the match point in the second grid model is determined according to the first colouring information, And the second color weight of the match point in third grid model is determined according to the second colouring information.

In operation S208, according to the first color weight, the second color weight and constraint weight and according to predetermined denoising model Surface denoising is carried out to the first grid model after normalization.

In accordance with an embodiment of the present disclosure, according to the first color weight, the second color weight and constraint weight and according to pre- Determine denoising model to after normalization the first grid model carry out surface denoising when, predetermined denoising model can be following public affairs Formula:

Wherein, the first color weight cn_iCharacterize match point in vertex and the third grid model in the first grid model Colour consistency, the second color weight cp_iCharacterize the face of match point in vertex and the second grid model in the first grid model Color consistency, present invention definition:

WhereinIt indicatesThe corresponding value of tri- Color Channel of RGB of point (between 0-1).Letter Number NN (i) characterizes the point in the second grid model and third grid model in the first grid model of distanceNearest point index.

In accordance with an embodiment of the present disclosure, according to the first color weight, the second color weight and constraint weight and according to pre- Determine denoising model to after normalization the first grid model carry out surface denoising when, adjacent continuous frame grid model match point The preset threshold T for meeting setting to the distance of present frame corresponding points can be following condition:

In accordance with an embodiment of the present disclosure, the colouring information of consecutive frame grid model corresponding points is increased into predetermined denoising model Weight in, not only considered the positional relationship of consecutive frame, but also taken into account the colour consistency of corresponding points, overcome at least in grid During model denoises, color and the inconsistent problem of master mould color after denoising improve present frame grid model Optimization ability is denoised, preferable effect of optimization has been reached.

In accordance with an embodiment of the present disclosure, by taking the denoising of the grid model of the sequential frame image of movement human as an example, make a reservation for denoising Model can be based on L₀The successive frame mesh denoising model of norm minimum, wherein be based on L₀The successive frame net of norm minimum The second grid model and third grid model bound term are included at least in lattice denoising model.It in accordance with an embodiment of the present disclosure, can be with The grid model p of current frame image is inputted in predetermined denoising model^f*And the grid model p of its before and after frames image^f-1And p^f+1, And initiation parameter α, beta, gamma, λ, γ_max, μ_α, μ_γ, with γ >=γ_maxFor stopping criterion for iteration.

In accordance with an embodiment of the present disclosure, shown by several groups of experiments the disclosure propose based on L₀The company of norm minimum Continue disposition of the grid model denoising method to the abnormal problem in model of frame image, in order to verify going for disclosure offer Method for de-noising is added to mesa-shaped protrusion, semicircle ball bumps and point to noise removal capability abnormal in model, respectively in grid model End protrusion, and simultaneously using the denoising method of the disclosure to these abnormal raised carry out denoisings.

Figure 12 a~12f is diagrammatically illustrated according to the embodiment of the present disclosure based on L₀The successive frame grid of norm minimum The Comparative result schematic diagram of the denoising method processing mesa-shaped protrusion front and back of denoising model.

Wherein, Figure 12 a be comprising random noise and abnormal raised original mesh model, Figure 12 b be using before and after frames about It is optimizing when Shu Quanchong is 0.1 as a result, Figure 12 c is using before and after frames constraint weight when being 0.5 result that optimizes.? In optimization process, the disclosure sets successive frame preset threshold as 0.2.In order to see the grid knot of model surface more obviously Structure, Figure 12 d (corresponding diagram 12a), Figure 12 e (corresponding diagram 12b) and Figure 12 f (corresponding diagram 12c) eliminate colouring information, increase net The Topology connection of lattice.From the optimum results in figure it can be found that after the constraint for considering before and after frames grid model, L₀Norm is most The multiframe mesh denoising model of smallization has certain smooth effect for the extremely raised phenomenon of mesa-shaped that individual models occur, and With the increase of consecutive frame weight, exception after optimization in model is raised can be further smooth.

Figure 13 a~13h diagrammatically illustrates the L using single frame according to the embodiment of the present disclosure₀Norm minimum grid Denoising method and it is different constraint weight based on L₀The denoising method of the successive frame mesh denoising model of norm minimum is to grid The Comparative result schematic diagram of the raised denoising of the spherical exception of semicircle in model.

Wherein, Figure 13 a eliminates colouring information, and the model display for being added to network topology connection is that Figure 13 e, Figure 13 b are gone Colouring information is fallen, the model display for being added to network topology connection is that Figure 13 f, Figure 13 c eliminate colouring information, is added to net The model display of LF Topology connection is that Figure 13 g and Figure 13 d eliminate colouring information, is added to the model exhibition of network topology connection It is shown as Figure 13 h, preferably to show model surface structure.Figure 13 a (corresponding with Figure 13 e) is that denoising is preceding comprising noise and abnormal convex The archetype risen, it can be seen that not only include numerous random noises at archetype surface, while there are one dome-types Exception it is raised, this protrusion is not present in same position of the grid model of consecutive frame.Figure 13 b (corresponding with Figure 13 f) It is using L₀Minimize mesh denoising method the model is individually denoised after as a result, L used in it₀Smoothing parameter λ is 0.001.It can be seen that relative to archetype, for the L of single frame model₀Mesh denoising method is minimized to including in model Random noise it is unsatisfactory for abnormal raised recovery effects.

Figure 13 c (corresponding with Figure 13 g) and Figure 13 d (corresponding with Figure 13 h) is proposed using the disclosure based on L₀Norm is most The denoising method of the successive frame mesh denoising model of smallization as a result, it is same use L₀Smoothing parameter λ is 0.001, and successive frame is pre- Determining threshold value is 0.2, and the constraint weight for the successive frame grid model that wherein Figure 13 c (corresponding with Figure 13 g) is used is 0.1, Figure 13 d The constraint weight for the successive frame grid model that (corresponding with Figure 13 h) uses is 0.5, passes through comparison secondary series, third column and the 4th The optimum results of column are it can be found that constrain weight by increasing successive frame grid model, the method that the disclosure proposes is to abnormal convex The recovery effects risen are better than the L of single frame₀Norm minimum mesh denoising method, and with the increasing of successive frame constraint weight Greatly, abnormal raised smoothness and recovery effects can also enhance.

Figure 14 a~14h diagrammatically illustrates the L using single frame according to the embodiment of the present disclosure₀Minimize mesh denoising Method and it is different constraint weight based on L₀The denoising method of the successive frame mesh denoising model of norm minimum is to sharp in model The Comparative result schematic diagram of the abnormal raised denoising in end.

Similar with Figure 13 a~13h, Figure 14 a (corresponding with Figure 14 e) is illustrated comprising noise and abnormal raised original mould Type, Figure 14 b (corresponding with Figure 14 f) illustrate single frame L₀Minimize mesh denoising result (L₀Smoothing parameter λ is 0.001) figure 14c (corresponding with Figure 14 g) illustrates the successive frame L that consecutive frame weight is 0.1₀Minimize mesh denoising result (L₀Smoothing parameter λ It is 0.001, consecutive frame distance threshold is that 0.2), Figure 14 d (corresponding with Figure 14 h) illustrates the successive frame that consecutive frame weight is 0.5 L₀Norm minimum mesh denoising result (L₀Smoothing parameter λ is 0.001,0.2) consecutive frame distance threshold is.It can be with from result It was found that it is extremely raised for tip-type, it is based on L₀The denoising method of the successive frame mesh denoising model of norm minimum is to tip The extremely raised treatment effect of type is than single frame L₀Minimizing mesh denoising method treatment effect will get well.

Figure 15 diagrammatically illustrates the block diagram of the surface denoising system according to the grid model of the embodiment of the present disclosure.

As shown in figure 15, the surface denoising system 300 of grid model includes that the first acquisition module 310, second obtains module 320 and first processing module 330.

First acquisition module 310 is used to obtain the first grid model of f frame image to be denoised.

Second acquisition module 320 is used to obtain the grid model with the image that f frame image is successive frame, wherein with f Frame image is that the image of successive frame includes f-1 frame image and/or f+1 frame image.

First processing module 330 is used for based on the grid model with the image that f frame image is successive frame, to the first grid Model carries out surface denoising.

In accordance with an embodiment of the present disclosure, the second acquisition module 320 be used to obtain the second grid model of f-1 frame image with The third grid model of f+1 frame image；And first processing module 330 is used for first to the second grid model, third grid mould Type and the first grid model are normalized, and the second grid model, third grid model and the first grid model are turned The model being changed under the same coordinate system, then carry out surface denoising.

Figure 16 diagrammatically illustrates the block diagram of the first processing module according to the embodiment of the present disclosure.

In accordance with an embodiment of the present disclosure, first processing module 330 is used for based on the second grid model and the after normalization Three grid models carry out surface denoising to the first grid model after normalization, wherein as shown in figure 16, the first processing Module 330 includes the first determination unit 331, the second determination unit 332 and first processing units 333.

First determination unit 331 is for determining in the second grid model and third grid model and in the first grid model The corresponding corresponding points in each vertex.

Second determination unit 332 is used to meet the distance between vertex and the corresponding corresponding points in each vertex in advance If the corresponding points of threshold value are determined as match point corresponding with the vertex.

First processing units 333 are used for according in the match point and third grid model in the second grid model determined Match point according to predetermined denoising model to the first grid model carry out surface denoising.

In accordance with an embodiment of the present disclosure, it can prevent the motive position of the grid model after optimization from occurring by preset threshold The problem of abnormal deformation.

Figure 17 diagrammatically illustrates the block diagram of the first processing module according to another embodiment of the disclosure.

As shown in figure 17, in accordance with an embodiment of the present disclosure, first processing module 330 in addition to include the first determination unit 331, It further include third determination unit 334 and the second processing unit 335 except second determination unit 332 and first processing units 333.

Third determination unit 334 is used to be unsatisfactory for the feelings of preset threshold at a distance from vertex is between corresponding corresponding points Under condition, it regard the vertex itself as the corresponding match point in the vertex.

The second processing unit 335 is used to carry out table to the first grid model according to predetermined denoising model according to the vertex itself Face denoising.

Figure 18 diagrammatically illustrates the block diagram of the surface denoising system of the grid model according to another embodiment of the disclosure.

In accordance with an embodiment of the present disclosure, predetermined denoising model includes being based on L₀The successive frame mesh denoising mould of norm minimum Type, wherein be based on L₀The second grid model and third grid are included at least in the successive frame mesh denoising model of norm minimum Model bound term, as shown in figure 18, the surface denoising system 300 of grid model including the first acquisition module 310, second in addition to obtaining It further include the first determining module 340 and Second processing module 350 except modulus block 320 and first processing module 330.

First determining module 340 is used to determine the constraint weight of the second grid model and third grid model bound term.

Second processing module 350 is used to carry out surface to the first grid model according to predetermined denoising model according to constraint weight Denoising.

Figure 19 diagrammatically illustrates the block diagram of the surface denoising system of the grid model according to another embodiment of the disclosure.

As shown in figure 19, in accordance with an embodiment of the present disclosure, the surface denoising system 300 of grid model including first in addition to obtaining Modulus block 310, second obtain module 320, first processing module 330, the first determining module 340 and Second processing module 350 it It outside, further include that third obtains module 360, the second determining module 370 and third processing module 380.

Third obtains the first colouring information and third grid that module 360 is used to obtain the match point in the second grid model Second colouring information of the match point in model.

Second determining module 370 is used to determine the first face of the match point in the second grid model according to the first colouring information Color weight, and determine according to the second colouring information the second color weight of the match point in third grid model.

Third processing module 380 is used for according to the first color weight, the second color weight and constraint weight and according to predetermined Denoising model carries out surface denoising to the first grid model after normalization.

It is understood that first obtains the acquisition of module 310, second module 320, first processing module 330, first determines Module 340, Second processing module 350, third obtain module 360, the second determining module 370, third processing module 380, first Determination unit 331, the second determination unit 332, first processing units 333, third determination unit 334 and the second processing unit 335 It may be incorporated in a module/unit and realize or any one module/unit therein can be split into multiple moulds Block/unit.Alternatively, at least partly function of one or more module/units in these module/units can be with other modules At least partly function combine, and realized in a module/unit.According to an embodiment of the invention, first obtains module 310, second module 320, first processing module 330, the first determining module 340, Second processing module 350, third acquisition are obtained Module 360, the second determining module 370, third processing module 380, the first determination unit 331, the second determination unit 332, first At least one of processing unit 333, third determination unit 334 and the second processing unit 335 can be at least at least partially implemented To be on hardware circuit, such as field programmable gate array (FPGA), programmable logic array (PLA), system on chip, substrate System, the system in encapsulation, specific integrated circuit (ASIC), or can be to carry out any other conjunction that is integrated or encapsulating to circuit The hardware such as reason mode realize with part, or are realized with software, the appropriately combined of hardware and firmware three kinds of implementations.Or Person, first, which obtains module 310, second, obtains module 320, first processing module 330, the first determining module 340, second processing mould Block 350, third obtain module 360, the second determining module 370, third processing module 380, the first determination unit 331, second really At least one of order member 332, first processing units 333, third determination unit 334 and the second processing unit 335 can be down to Be implemented partly as computer program module/unit less, when the program is run by computer, can execute corresponding module/ The function of unit.

It should be noted that in embodiment of the disclosure surface the denoising system part and the disclosure of grid model implementation The surface denoising method part of grid model is corresponding in example, and the description of the surface denoising system part of grid model is specific The surface denoising method part of grid of reference model, details are not described herein.

Another aspect of the disclosure provides a kind of computer system, including one or more processors；Memory is used In storing one or more programs, wherein when one or more programs are executed by one or more processors so that one or Multiple processors realize the surface denoising method of grid model as described above.

Figure 20 diagrammatically illustrates the meter of the surface denoising method for being adapted for carrying out grid model according to the embodiment of the present disclosure The block diagram of calculation machine system.Computer system shown in Figure 20 is only an example, should not function to the embodiment of the present disclosure and Use scope brings any restrictions.

It as shown in figure 20, include processor 401 according to the computer system of the embodiment of the present disclosure 400, it can be according to depositing Storage is loaded into random access storage device (RAM) 403 in the program in read-only memory (ROM) 402 or from storage section 408 Program and execute various movements appropriate and processing.Processor 401 for example may include general purpose microprocessor (such as CPU), Instruction set processor and/or related chip group and/or special microprocessor (for example, specific integrated circuit (ASIC)), etc..Place Reason device 401 can also include the onboard storage device for caching purposes.Processor 401 may include for executing with reference to Fig. 3, figure 4, the single treatment units of the different movements of the method flow according to the embodiment of the present disclosure of Fig. 7, Fig. 8 and Figure 11 description are either Multiple processing units.

In RAM 403, it is stored with system 400 and operates required various programs and data.Processor 401, ROM 402 with And RAM 403 is connected with each other by bus 404.Processor 401 is held by executing the program in ROM 402 and/or RAM 403 The various operations that row is described above with reference to Fig. 3, Fig. 4, Fig. 7, Fig. 8 and Figure 11.It is being removed it is noted that described program also can store In one or more memories other than ROM 402 and RAM 403.Processor 401 can also be stored in described one by executing Program in a or multiple memories executes the various operations described above with reference to Fig. 3, Fig. 4, Fig. 7, Fig. 8 and Figure 11.

In accordance with an embodiment of the present disclosure, system 400 can also include input/output (I/O) interface 404, input/output (I/O) interface 405 is also connected to bus 404.System 400 can also include be connected to I/O interface 405 with one in lower component Item is multinomial: the importation 406 including keyboard, mouse etc.；Including such as cathode-ray tube (CRT), liquid crystal display (LCD) Deng and loudspeaker etc. output par, c 407；Storage section 408 including hard disk etc.；And including such as LAN card, modulatedemodulate Adjust the communications portion 409 of the network interface card of device etc..Communications portion 409 executes communication process via the network of such as internet. Driver 410 is also connected to I/O interface 405 as needed.Detachable media 411, such as disk, CD, magneto-optic disk, semiconductor Memory etc. is mounted on as needed on driver 410, in order to be pacified as needed from the computer program read thereon It is packed into storage section 408.

In accordance with an embodiment of the present disclosure, it may be implemented as computer software journey above with reference to the method for flow chart description Sequence.For example, embodiment of the disclosure includes a kind of computer program product comprising be carried on computer readable storage medium Computer program, which includes the program code for method shown in execution flow chart.In such implementation In example, which can be downloaded and installed from network by communications portion 409, and/or from detachable media 411 It is mounted.When the computer program is executed by processor 401, the above-mentioned function limited in the system of the embodiment of the present disclosure is executed Energy.In accordance with an embodiment of the present disclosure, system as described above, unit, module, unit etc. can pass through computer program Module is realized.

It should be noted that computer readable storage medium shown in the disclosure can be computer-readable signal media or Person's computer readable storage medium either the two any combination.Computer readable storage medium for example can be --- But be not limited to --- electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor system, device or device, or any above group It closes.The more specific example of computer readable storage medium can include but is not limited to: have being electrically connected for one or more conducting wires Connect, portable computer diskette, hard disk, random access storage device (RAM), read-only memory (ROM), erasable type may be programmed it is read-only Memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), light storage device, magnetic memory Part or above-mentioned any appropriate combination.In the disclosure, computer readable storage medium, which can be, any include or stores The tangible medium of program, the program can be commanded execution system, device or device use or in connection.And In the disclosure, computer-readable signal media may include in a base band or the data as the propagation of carrier wave a part are believed Number, wherein carrying computer-readable program code.The data-signal of this propagation can take various forms, including but not It is limited to electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be computer Any computer readable storage medium other than readable storage medium storing program for executing, which can send, propagate or Person's transmission is for by the use of instruction execution system, device or device or program in connection.It is computer-readable to deposit The program code for including on storage media can transmit with any suitable medium, including but not limited to: wireless, electric wire, optical cable, RF Etc. or above-mentioned any appropriate combination.In accordance with an embodiment of the present disclosure, on computer readable storage medium may include One or more memories other than the ROM 402 and/or RAM 403 and/or ROM 402 and RAM 403 of text description.

Flow chart and block diagram in attached drawing are illustrated according to the system of the various embodiments of the disclosure, method and computer journey The architecture, function and operation in the cards of sequence product.In this regard, each box in flowchart or block diagram can generation A part of one module, program segment or code of table, a part of above-mentioned module, program segment or code include one or more Executable instruction for implementing the specified logical function.It should also be noted that in some implementations as replacements, institute in box The function of mark can also occur in a different order than that indicated in the drawings.For example, two boxes succeedingly indicated are practical On can be basically executed in parallel, they can also be executed in the opposite order sometimes, and this depends on the function involved.Also it wants It is noted that the combination of each box in block diagram or flow chart and the box in block diagram or flow chart, can use and execute rule The dedicated hardware based systems of fixed functions or operations is realized, or can use the group of specialized hardware and computer instruction It closes to realize.

As on the other hand, the disclosure additionally provides a kind of computer readable storage medium, the computer-readable storage medium Matter can be included in equipment described in above-described embodiment；It is also possible to individualism, and without in the supplying equipment. Executable instruction is stored on computer readable storage medium, which makes processor execute grid model when being executed by processor Surface denoising method, this method includes the first grid model for obtaining f frame image to be denoised；It obtains and above-mentioned f frame Image is the grid model of the image of successive frame, wherein above-mentioned to include f-1 with above-mentioned f frame image is successive frame image Frame image and/or f+1 frame image；It is right and based on the above-mentioned grid model with the image that above-mentioned f frame image is successive frame Above-mentioned first grid model carries out surface denoising.Optionally, the net with the image that above-mentioned f frame image is successive frame is obtained Lattice model includes obtaining the second grid model of above-mentioned f-1 frame image and the third grid model of above-mentioned f+1 frame image；With And carrying out surface denoising to above-mentioned first grid model includes first to above-mentioned second grid model, above-mentioned third grid model It is normalized with above-mentioned first grid model, by above-mentioned second grid model, above-mentioned third grid model and above-mentioned First grid model is converted to the model under the same coordinate system, then carries out surface denoising.Optionally, based on it is above-mentioned with it is above-mentioned F frame image is the grid model of the image of successive frame, and carrying out surface denoising to above-mentioned first grid model includes: to be based on Above-mentioned second grid model and above-mentioned third grid model after normalization carry out above-mentioned first grid model after normalization Surface denoising, wherein carrying out surface denoising to above-mentioned first grid model after normalization includes determining above-mentioned the Corresponding points corresponding with each vertex in above-mentioned first grid model in two grid models and above-mentioned third grid model；It will The corresponding points that the distance between vertex in above-mentioned each vertex and corresponding corresponding points meet preset threshold be determined as with should The corresponding match point in vertex；And according to the match point and above-mentioned third grid mould in above-mentioned second grid model determined Match point in type carries out surface denoising to above-mentioned first grid model according to predetermined denoising model.Optionally, above-mentioned net The surface denoising method of lattice model further includes being unsatisfactory for above-mentioned preset threshold at a distance from vertex is between corresponding corresponding points In the case where, it regard the vertex itself as the corresponding match point in the vertex；And according to the vertex itself according to above-mentioned predetermined Denoising model carries out surface denoising to above-mentioned first grid model.Optionally, above-mentioned predetermined denoising model includes being based on L₀ The successive frame mesh denoising model of norm minimum, wherein above-mentioned to be based on L₀The successive frame mesh denoising model of norm minimum In include at least above-mentioned second grid model and above-mentioned third grid model bound term, the above method further includes determining above-mentioned second The constraint weight of grid model and above-mentioned third grid model bound term；And make a reservation for go according to above-mentioned according to above-mentioned constraint weight Model of making an uproar carries out surface denoising to above-mentioned first grid model.Optionally, the surface denoising method of above-mentioned grid model is also Match point in the first colouring information and above-mentioned third grid model including obtaining the match point in above-mentioned second grid model The second colouring information；The first color power of the match point in above-mentioned second grid model is determined according to above-mentioned first colouring information Again, and according to above-mentioned second colouring information the second color weight of the match point in above-mentioned third grid model is determined；And root According to above-mentioned first color weight, above-mentioned second color weight and above-mentioned constraint weight and according to above-mentioned predetermined denoising model to normalizing Above-mentioned first grid model after change carries out surface denoising.

Embodiment of the disclosure is described above.But the purpose that these embodiments are merely to illustrate that, and It is not intended to limit the scope of the present disclosure.Although respectively describing each embodiment above, but it is not intended that each reality Use cannot be advantageously combined by applying the measure in example.The scope of the present disclosure is defined by the appended claims and the equivalents thereof.It does not take off From the scope of the present disclosure, those skilled in the art can make a variety of alternatives and modifications, these alternatives and modifications should all fall in this Within scope of disclosure.

Claims

1. a kind of surface denoising method of grid model, comprising:

Obtain the first grid model of f frame image to be denoised；

Obtain the grid model with the image that the f frame image is successive frame, wherein the described and f frame image is to connect The image of continuous frame includes f-1 frame image and/or f+1 frame image；And

Based on the grid model with the image that the f frame image is successive frame, table is carried out to first grid model Face denoising.

2. according to the method described in claim 1, wherein:

Obtaining with the grid model for the image that the f frame image is successive frame includes obtain the f-1 frame image second The third grid model of grid model and the f+1 frame image；And

Carrying out surface denoising to first grid model includes first to second grid model, the third grid mould Type and first grid model are normalized, by second grid model, the third grid model and institute It states the first grid model and is converted to model under the same coordinate system, then carry out surface denoising.

3. according to the method described in claim 2, being the net of the image of successive frame based on the described and f frame image wherein Lattice model, carrying out surface denoising to first grid model includes:

Based on second grid model and the third grid model after normalization, to first grid after normalization Model carries out surface denoising, wherein carrying out surface denoising to first grid model after normalization includes:

Determine in second grid model and the third grid model with each vertex phase in first grid model Corresponding corresponding points；

The corresponding points that the distance between vertex and corresponding corresponding points in each vertex meet preset threshold are determined For match point corresponding with the vertex；And

According to the match point in the match point and the third grid model in second grid model determined according to pre- Determine denoising model and surface denoising is carried out to first grid model.

4. according to the method described in claim 3, wherein, the method also includes:

In the case where being unsatisfactory for the preset threshold at a distance from vertex is between corresponding corresponding points, the vertex itself is made For the corresponding match point in the vertex；And

Surface denoising is carried out to first grid model according to the predetermined denoising model according to the vertex itself.

5. according to the method described in claim 3, wherein, the predetermined denoising model includes being based on L₀Norm minimum it is continuous Frame mesh denoising model, wherein described to be based on L₀Described the is included at least in the successive frame mesh denoising model of norm minimum Two grid models and the third grid model bound term, the method also includes:

Determine the constraint weight of second grid model and the third grid model bound term；And

Surface denoising is carried out to first grid model according to the predetermined denoising model according to the constraint weight.

6. according to the method described in claim 5, wherein, the method also includes:

Obtain the match point in the first colouring information and the third grid model of the match point in second grid model The second colouring information；

The first color weight of the match point in second grid model is determined according to first colouring information, and according to institute State the second color weight of the match point that the second colouring information determines in the third grid model；And

According to first color weight, second color weight and the constraint weight and according to the predetermined denoising model Surface denoising is carried out to first grid model after normalization.

7. a kind of surface denoising system of grid model, comprising:

First obtains module, for obtaining the first grid model of f frame image to be denoised；

Second obtain module, for obtains with the f frame image be successive frame image grid model, wherein it is described and The f frame image is that the image of successive frame includes f-1 frame image and/or f+1 frame image；And

First processing module, for based on it is described with the f frame image be successive frame image grid model, to described the One grid model carries out surface denoising.

8. system according to claim 7, in which:

The second acquisition module is used to obtain the second grid model and the f+1 frame image of the f-1 frame image Third grid model；And

The first processing module is used for first to second grid model, the third grid model and the first grid mould Type is normalized, and second grid model, the third grid model and first grid model are converted For the model under the same coordinate system, then carry out surface denoising.

9. system according to claim 8, wherein the first processing module is used for based on described second after normalization Grid model and the third grid model carry out surface denoising to first grid model after normalization, wherein The first processing module includes:

First determination unit, for determine in second grid model and the third grid model with the first grid mould The corresponding corresponding points in each vertex in type；

Second determination unit, for presetting the vertex in each vertex with the distance between corresponding corresponding points satisfaction The corresponding points of threshold value are determined as match point corresponding with the vertex；And

First processing units, for according to the match point and the third grid model in second grid model determined In match point according to predetermined denoising model to first grid model carry out surface denoising.

10. system according to claim 9, wherein the first processing module further include:

Third determination unit, the case where for being unsatisfactory for the preset threshold at a distance from vertex is between corresponding corresponding points Under, it regard the vertex itself as the corresponding match point in the vertex；And

The second processing unit, for being carried out according to the predetermined denoising model to first grid model according to the vertex itself Surface denoising.

11. system according to claim 9, wherein the predetermined denoising model includes being based on L₀Norm minimum it is continuous Frame mesh denoising model, wherein described to be based on L₀Described the is included at least in the successive frame mesh denoising model of norm minimum Two grid models and the third grid model bound term, the system also includes:

First determining module, for determining the constraint weight of second grid model and the third grid model bound term； And

Second processing module, for according to the constraint weight according to the predetermined denoising model to first grid model into The denoising of row surface.

12. system according to claim 11, wherein the system also includes:

Third obtains module, for obtaining the first colouring information and the third net of the match point in second grid model Second colouring information of the match point in lattice model；

Second determining module, for determining first of the match point in second grid model according to first colouring information Color weight, and determine according to second colouring information the second color weight of the match point in the third grid model； And

Third processing module, for according to first color weight, second color weight and the constraint weight and pressing Surface denoising is carried out to first grid model after normalization according to the predetermined denoising model.

13. a kind of computer system, comprising:

One or more processors；

Memory, for storing one or more programs,

Wherein, when one or more of programs are executed by one or more of processors, so that one or more of Processor realizes the surface denoising method of grid model described in any one of claims 1 to 6.

14. a kind of computer readable storage medium, is stored thereon with executable instruction, which makes to handle when being executed by processor Device realizes the surface denoising method of grid model described in any one of claims 1 to 6.