CN110148220A - The three-dimensional rebuilding method of the big object of the interior space - Google Patents
The three-dimensional rebuilding method of the big object of the interior space Download PDFInfo
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- CN110148220A CN110148220A CN201811002769.1A CN201811002769A CN110148220A CN 110148220 A CN110148220 A CN 110148220A CN 201811002769 A CN201811002769 A CN 201811002769A CN 110148220 A CN110148220 A CN 110148220A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/10—Constructive solid geometry [CSG] using solid primitives, e.g. cylinders, cubes
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T19/00—Manipulating 3D models or images for computer graphics
- G06T19/20—Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
Abstract
The invention discloses a kind of three-dimensional rebuilding methods of the big object of interior space, including establish big object feature learning library, each big object feature has corresponding spatial relationship；Currently pending image is obtained, carries out deep learning with big object feature learning library, obtains the spatial relationship of big object feature in present image；The spatial relationship of big object feature is applied to lines image or lines-super-pixel image by the lines image or lines-super-pixel image for obtaining currently pending image, and the lines in big object feature region are deleted；It will be blocked by big object and the lines interrupted caused to be fitted by force.In the present invention, when lines are blocked in lines-super-pixel image, by haling straight fitting to lines, the big object feature in space is accurately rebuild.
Description
Technical field
The present invention relates to a kind of three-dimensional rebuilding methods of the big object of interior space.
Background technique
Following background technique is used to help reader and understands the present invention, and is not construed as the prior art.
Three-dimensional panorama is the real scene virtual reality technology based on panoramic picture.Panorama is that 360 ° of camera ring are shot
One or more groups of photos are spliced into a panoramic picture, can also be by once shooting realization panoramic picture, and panoramic picture is one
Kind carries out the plane picture of mapping production to ambient enviroment, object with certain geometrical relationship, and panoramic picture needs to carry out Three-dimensional Gravity
Three-dimension space image could be become by building.Three-dimensional panorama figure is generally captured the image of entire scene by general camera combination fish eye lens
Information reuses software and carries out picture split, becomes 360 ° of panoramic pictures and browses for virtual reality.
But panoramic pictures are to show three-dimensional scenic by two-dimensional mode, the three-dimensional space sense missing of image, and companion
And have lines distort (such as straight line shows as curve in panoramic pictures).There is a kind of side that panoramic pictures are redeveloped into three-dimensional scenic
Method is: obtaining panoramic picture in input chamber, obtains lines in such a way that discrete point is fitted, mark different type by image, semantic
Lines；Generate super-pixel by segmentation, mark each face direction (such as with color indicia, red expression floor or smallpox
The horizontal planes such as plate, striped color table show the vertical planes such as metope, and white indicates the face for not applying direction limitation), restore the depth of image
Information obtains grayscale image；Rebuild three-dimensional lines, output three-dimensional space model.The shortcomings that this method for reconstructing three-dimensional scene, is:
When lines in lines-super-pixel image are blocked, the big object feature in space can not be accurately rebuild.
Summary of the invention
The purpose of the present invention is to provide a kind of three-dimensional rebuilding method of the big object of interior space, lines-super-pixel image
When middle lines are blocked, by haling straight fitting to lines, the big object feature in space is accurately rebuild.
The technical solution adopted by the present invention to solve the technical problems is: the three-dimensional rebuilding method of the big object of the interior space,
The following steps are included:
S3.2.1, big object feature learning library is established, each big object feature has corresponding spatial relationship, big object feature
Include: furniture, household electrical appliances etc., the spatial relationship of big object refers to: the region segmentation of big object and each correlation plane is closed in the picture
System；
S3.2.2, currently pending image being obtained, currently pending image can be panoramic picture and be also possible to single-view image,
Deep learning is carried out with big object feature learning library, obtains the spatial relationship of big object feature in present image；
S3.2.3, the lines image or lines-super-pixel image for obtaining currently pending image, the space of big object feature is closed
System is applied to lines image or lines-super-pixel image, and the lines in big object feature region are deleted；
S3.2.4, it will be blocked by big object and the lines interrupted caused to be fitted by force；Block by big object referring to this
The terminal or starting point of lines are located on the boundary of big object；Strong fitting refers to: being extended forward using current line rule, searching is
No have Extending Law therewith identical and what distance was less than preset value is fitted lines；This can be fitted lines if it exists, this will be current
It lines and lines can be fitted permeates a lines.
The present invention has the advantages that when the lines in lines-super-pixel image are blocked, it is straight quasi- by being haled to lines
Close, on the door, the three-dimensional reconstruction precision of window, large object it is high, speed is fast.
Detailed description of the invention
Fig. 1 is the three-dimensional panoramic image of a separate space.
Fig. 2 is lines-super-pixel image of three-dimensional panoramic image.
Fig. 3 is that three-dimensional panoramic image decomposes the wherein single-view image obtained.
Fig. 4 is the corresponding spatial relationship of Fig. 3.
Fig. 5 is the corresponding spatial relationship of three-dimensional panoramic image.
Fig. 6 is the lines image after lines detection and fitting.
Fig. 7 is the corresponding three-dimensional space of Fig. 1.
Fig. 8 is the spatial relationship deep learning of big object feature.
Fig. 9 is the schematic diagram for needing more Space integrations.
Specific embodiment
The present invention is further described with reference to the accompanying drawings and detailed description.
The three-dimensional reconstruction of the interior space
In some embodiments, a kind of interior space three-dimensional rebuilding method as shown in figs. 1-7, comprising the following steps:
S1, corner feature learning library is established, each corner feature has corresponding spatial relationship, the spatial relationship of corner feature
Refer to, in the corner image, ceiling, metope and the distributed relation on ground, the image of corner feature are cameras from fixed angle
The single-view image of shooting；
S2, current panorama image to be reconstructed is obtained；
S3, lines detection and/or super-pixel segmentation are done to current panorama image by image, semantic analysis different colours or not
Synteny indicates the lines of different directions, generates the lines-super-pixel image being made of lines and super-pixel；
S4, the spatial relationship for obtaining current panorama image:
S4.1, current panorama image is resolved into multiple single-view images by visual angle；Panoramic picture generation is needed using multiple not
Image with visual angle is synthesized, multiple the single-view images being decomposed to form are exactly the image for synthesizing panoramic picture；
S4.2, the deep learning that every single-view image is carried out to corner feature learning library respectively, are identified by deep learning
The spatial relationship of corner feature；
S4.3, the spatial relationship of all single-view images is synthesized by the synthesis condition of panoramic picture, generates current panorama
The spatial relationship of image indicates different spatial positions (spatial position such as ceiling, metope, ground etc.) in different colors；A table
Show that the parallel lines of same color, b indicate that the parallel lines of same color, c indicate the parallel lines of same color, such as Fig. 2-4
It is shown；
S5, the spatial relationship of current panorama image is applied to lines-super-pixel image, deletes side in lines-super-pixel image
It is obtained to the lines of mistake, generates the lines-super-pixel image with spatial relationship；Such as, indicate that the lines of metope appear in
Ground or ceiling region, then the line orientations mistake of the expression metope, does delete processing；The spatial relationship of current panorama image
, lines consistent with the pixel coordinate of current panorama figure-super-pixel image is consistent with the pixel coordinate of current panorama figure, because
This, the spatial relationship of current panorama image can be mapped with lines-super-pixel image；
S6, with the information in each face in the lines with spatial relationship-super-pixel image reconstruction space；
Step S3 is synchronous with S4 to carry out, and perhaps first progress S3 carries out S4 again or first progress S4 carries out S3 again.
In some embodiments, the fitting of multistage detection lines is carried out to the lines in step S3-super-pixel image, including
Following steps:
S3.1.1, lines detection acquisition lines image is carried out to current panorama image, by semantic analysis, mark different directions
The algorithm of lines, lines detection uses common algorithm, such as SFV；
S3.1.2, the rule that any one lines in lines image extend forward as current line, acquisition current line are obtained
It restrains (such as slope, arc curvature of a curve of straight line etc.), current line is extended forward by its Extending Law；
S3.1.3, as current line extends forward, judge whether there is at a distance from current line extended segment less than preset value
Combinable lines, if so, current line and combinable lines are fused into a lines；If it is not, then retaining working as of not extending
Preceding lines.
As a preferred option, when space three-dimensional is rebuild indoors, the line of anisotropy in lines-super-pixel image is deleted
The detection and fitting of lines are carried out after item again.
As a preferred option, in step S3.1.3, the method for lines fusion are as follows: current line extends forward, encounter can
When merging lines, continuous fitting lines are fitted in the terminal of starting point to the combinable lines of current line, continuous lines
The error of the Extending Law of fitting rule and current line is in the error range of setting；As current line and fitting lines are
Camber line, then the difference between the radius of curvature of current line and the radius of curvature for being fitted lines should be in the error range of setting；
Fitting lines may be or the fitting lines and current line or combinable line between current line and combinable lines
One of item is overlapped；
Alternatively, the terminal of combinable lines is moved to current line when current line extends forward, encounters combinable lines
Extended segment forms fitting lines；
Fitting lines extend forward according to the continuation of its Extending Law, if protecting to combinable lines are still encountered at the end of plane
Stay the current line not extended；If to combinable line segment has been merged at the end of plane, with fitting lines substitution current line and
All combinable line segments merged.
In some embodiments, in lines-super-pixel image, when there is the case where lines are blocked, to the strong of lines
Fitting is straightened, comprising the following steps:
S3.2.1, big object feature learning library is established, each big object feature has corresponding spatial relationship, big object feature
Include: furniture, household electrical appliances etc., the spatial relationship of big object refers to: the region segmentation of big object and each correlation plane is closed in the picture
System；
S3.2.2, current panorama image is obtained, is that current panorama image with big object feature learning library carries out deep learning, acquisition
The spatial relationship of big object feature, as shown in Figure 8；
S3.2.3, the lines image or lines-super-pixel image for obtaining current panorama image, by the spatial relationship of big object feature
It is applied to lines image or lines-super-pixel image, the lines in big object feature region are deleted；
S3.2.4, it will be blocked by big object and the lines interrupted caused to be fitted by force；Block by big object referring to this
The terminal or starting point of lines are located on the boundary of big object；Strong fitting refers to: being extended forward using current line rule, searching is
No have Extending Law therewith identical and what distance was less than preset value is fitted lines；
Alternatively, the end boundary in face where extending forwardly to lines using current line rule, judges current line and adjacent surface
The adjacent surface for whether having distance to be less than preset value can be fitted lines, and adjacent surface, which can be fitted lines, to be passed through positioned at the lines of adjacent surface
Its Extending Law extend forwardly to the boundary of the adjacent surface or the adjacent surface can be fitted lines be actually to terminate at the adjacent surface
Boundary；The lines for belonging to the same end point or direction of extinction belong to the same face.End point is the existing skill of this field
Art, existing paper disclose the detailed theory of end point, not reinflated explanation in the application.
In some embodiments, the lines after over-fitting-super-pixel image is as the super picture of lines-used in step S5
Sketch map picture.
Lines approximating method
Above method identifies corner feature and its spatial relationship in panoramic picture, often by learning to corner depths of features
A corner feature is corresponding with ceiling, metope and the space on ground distributed relation, and the spatial relationship group of all corner features closes
Come, can complete to build out corresponding three-dimensional space frame (such as smallpox of panoramic picture to the spatial relationship difference of current panorama image
Plate region, wall section, ground region)；Line orientations indicate intersection or parallel relation between metope, and spatial relationship combines
Lines-super-pixel image reconstruct the corresponding three-dimensional space of panoramic picture.
In some embodiments, for lines image or lines-super-pixel image, when carrying out lines detection, existing picture
Vegetarian refreshments fitting often occurs that lines interrupt, incomplete problem provides a kind of lines detection and be fitted to obtain complete lines
Method.
A kind of method of lines fitting when image reconstruction, comprising the following steps:
S3.1, lines detection acquisition lines image, the line for passing through semantic analysis, marking different directions are carried out to current panorama image
The algorithm of item, lines detection uses common algorithm, such as SFV；
S3.2, the rule that any one lines in lines image extend forward as current line, acquisition current line is obtained
(such as slope, arc curvature of a curve of straight line etc.), current line is extended forward by its Extending Law；
S3.3, as current line extends forward, judge whether there is at a distance from current line extended segment less than preset value can
Merge lines, if so, current line and combinable lines are fused into a lines；If it is not, then retain do not extend it is current
Lines.
As a preferred option, when space three-dimensional is rebuild indoors, the line of anisotropy in lines-super-pixel image is deleted
The detection and fitting of lines are carried out after item again.
As a preferred option, the method for current line Extending Law forward is obtained in step S3.2 are as follows: obtain camber line
The slope of curvature or straight line.
As a preferred option, in step S3.3, the method for lines fusion are as follows: current line extends forward, encounters and can close
And when lines, continuous fitting lines are fitted in the terminal of starting point to the combinable lines of current line, continuous lines are intended
The error of the Extending Law of rule and current line is closed in the error range of setting；If current line and fitting lines are arc
Line, then the difference between the radius of curvature of current line and the radius of curvature for being fitted lines should be in the error range of setting；It is quasi-
Zygonema item may be or the fitting lines and current line or combinable lines between current line and combinable lines
One of them is overlapped；
Alternatively, the terminal of combinable lines is moved to current line when current line extends forward, encounters combinable lines
Extended segment forms fitting lines；
Fitting lines extend forward according to the continuation of its Extending Law, if protecting to combinable lines are still encountered at the end of plane
Stay the current line not extended；If to combinable line segment has been merged at the end of plane, with fitting lines substitution current line and
All combinable line segments merged.
Big object feature
When there is the case where lines are blocked, straight fitting is haled to lines, comprising the following steps:
S3.2.1, big object feature learning library is established, each big object feature has corresponding spatial relationship, big object feature
Include: furniture, household electrical appliances etc., the spatial relationship of big object refers to: the region segmentation of big object and each correlation plane is closed in the picture
System；
S3.2.2, currently pending image being obtained, currently pending image can be panoramic picture and be also possible to single-view image,
Deep learning is carried out with big object feature learning library, obtains the spatial relationship of big object feature in present image；
S3.2.3, the lines image or lines-super-pixel image for obtaining currently pending image, the space of big object feature is closed
System is applied to lines image or lines-super-pixel image, and the lines in big object feature region are deleted；
S3.2.4, it will be blocked by big object and the lines interrupted caused to be fitted by force；Block by big object referring to this
The terminal or starting point of lines are located on the boundary of big object；Strong fitting refers to: being extended forward using current line rule, searching is
No have Extending Law therewith identical and what distance was less than preset value is fitted lines；This can be fitted lines if it exists, this will be current
It lines and lines can be fitted permeates a lines.
More Space integrations
In three-dimensional Reconstruction, it is possible to there are more spaces and need the case where merging, such as a large space be divided into it is more
It when a small space, then needs all small Space integrations are integral, in this case, needs to carry out the fusion of three-dimensional space.
As a preferred option, as shown in figure 9, the fusion method in more spaces, comprising the following steps:
SI, scale is puted up in each small space before taking pictures；
SII, scale learning database is established；
SIII, the panoramic picture in each space is obtained as current spatial image；
SIV, current spatial image is subjected to deep learning with scale learning database, finds out the scale in current spatial image, by institute
There is the scale of spatial image to carry out the registration in direction and size, registration refers to that the scale of all spatial images is completely coincident；
SV, three-dimensional Reconstruction is carried out to each panoramic picture after scale registration respectively.
Preferably, in SV, the three-dimensional space face with identical information is synthesized into a face, thus by multiple small space combinations
The three-dimensional graph in space more than one.
The fusion method in more spaces takes pictures to each subspace without using same model camera, different product can be used
Board, different cameral respectively take pictures to each subspace, as long as the scale puted up in each space is consistent.
When more Space integrations, the three-dimensional reconstruction of every sub-spaces uses the three-dimensional space reconstruction side in the various embodiments described above
Method.
This method finds the scale in each panoramic picture by deep learning, can be complete after all scales are registrated
The pairs of direction in each space and the registration of size, the panoramic picture after registration can be reconstructed into the three-dimensional space of same ratio, then
Multiple three-dimensional space are merged.
In the case where lacking any element specifically disclosed herein, limitation, may be implemented illustrated and described herein
Invention.Used terms and expressions method is used as the term of explanation rather than limits, and is not intended in these terms and table
Up to any equivalent for excluding shown and described feature or part thereof in the use of method, and it should be realized that various remodeling exist
It is all feasible in the scope of the present invention.It is therefore to be understood that although specifically being disclosed by various embodiments and optional feature
The present invention, but the modifications and variations of concept as described herein can be used by those of ordinary skill in the art, and recognize
It is fallen into for these modifications and variations within the scope of the present invention of the appended claims restriction.
It is described herein or record article, patent, patent application and every other document and can electronically obtain
The content of information to a certain extent in full include herein by reference, just as each individual publication by specific and single
Solely point out by reference.Applicant retains from any of any this article, patent, patent application or other documents
And all material and information are incorporated into the right in the application.
Claims (1)
1. the three-dimensional rebuilding method of the big object of the interior space, it is characterised in that: the following steps are included:
S3.2.1, big object feature learning library is established, each big object feature has corresponding spatial relationship；
S3.2.2, currently pending image being obtained, currently pending image can be panoramic picture and be also possible to single-view image,
Deep learning is carried out with big object feature learning library, obtains the spatial relationship of big object feature in present image；
S3.2.3, the lines image or lines-super-pixel image for obtaining currently pending image, the space of big object feature is closed
System is applied to lines image or lines-super-pixel image, and the lines in big object feature region are deleted；
S3.2.4, it will be blocked by big object and the lines interrupted caused to be fitted by force；Block by big object referring to this
The terminal or starting point of lines are located on the boundary of big object；Strong fitting refers to: being extended forward using current line rule, searching is
No have Extending Law therewith identical and what distance was less than preset value is fitted lines；This can be fitted lines if it exists, this will be current
It lines and lines can be fitted permeates a lines.
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