CN109801367B - Grid model characteristic editing method based on compressed manifold mode - Google Patents
Grid model characteristic editing method based on compressed manifold mode Download PDFInfo
- Publication number
- CN109801367B CN109801367B CN201910135990.2A CN201910135990A CN109801367B CN 109801367 B CN109801367 B CN 109801367B CN 201910135990 A CN201910135990 A CN 201910135990A CN 109801367 B CN109801367 B CN 109801367B
- Authority
- CN
- China
- Prior art keywords
- model
- skeleton
- characteristic
- mesh
- mesh model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Landscapes
- Processing Or Creating Images (AREA)
Abstract
The invention discloses a grid model characteristic editing method based on a compressed manifold mode, which comprises the following steps: 1) Acquiring basic parameters of a triangular mesh model; 2) Performing reconstruction calculation on the triangular mesh model by using a compressed manifold mode to obtain a characteristic skeleton model of the triangular mesh model; 3) According to user interaction operation, performing deformation editing on a characteristic skeleton model of the triangular mesh model, and calculating to obtain a skeleton model after deformation editing; 4) Performing skeleton model smoothing processing on the skeleton model after deformation editing through calculation; 5) Adding mesh model details to the feature skeleton model edited by deformation according to the feature skeleton model constructed by the original mesh model; 6) And repairing the grid model added with the details based on the differential coordinates of the original grid model to obtain the grid model after deformation editing. The invention realizes the construction of the characteristic skeleton of the grid model based on the compressed manifold mode for the first time, and adopts the differential coordinates for keeping the original grid model, so that the edited grid model is more real.
Description
Technical Field
The invention relates to the technical field of digital geometric processing, in particular to a grid model characteristic editing method based on a compressed manifold mode.
Background
In modern times, with the rapid development of science and technology, the computer field is rapidly developed day by day, and with the breakthrough of the hardware field, the software field is also rapidly developed. People's lives have increased and more high-tech products have appeared in people's lives, of which a large number are derived from computer graphics. From various high-tech technologies such as three-dimensional animation movies and game scenes with extremely good reality in movie and television entertainment, production and scientific research fields such as virtual experiments and simulated scene analysis data, product modeling design by applying computer-aided geometric design and the like, the three-dimensional digital geometric model is found to be widely applied in more and more industry fields. The three-dimensional digital geometric model is also paid more and more attention, and in the process of constructing the three-dimensional geometric model, it is found that many mathematical geometric problems which need to be processed are usually involved, for example, the problems of denoising (denoising) data, simplifying (simplifying) data, parameterizing (parameterizing) data, deforming and editing (deforming or editing) a grid model, segmenting (segmenting) the grid model, shape analysis and retrieval (shape analysis and retrieval) of the grid model, and the like are performed on the data. These problems are the main problems in the study processing of the digital geometry processing, and they constitute the main content of the study of the digital geometry processing. The representation of a three-dimensional object by a computer usually requires obtaining a representation of its geometric model, and the representation of a geometric object can be represented by a mathematical spline function or an implicit function. The construction of three-dimensional models is taken as the basis of computer graphics, which is the premise of computer graphics research on other problems. There are many methods we know to construct three-dimensional models at present, and the NURBS (non-uniform rational B-spline, bezier curve surface) method can be used for Computer Aided Design (CAD); the model can also be directly constructed manually by using three-dimensional modeling software Autodesk 3D Maxs; the model can be scanned by a depth camera, and the reconstruction of the model and the like can be completed by the acquisition of the point cloud. At present, although a large number of three-dimensional model construction methods exist, the three-dimensional model construction methods are relatively complex and complicated to use, and are not suitable for general household users to construct three-dimensional models.
It is relatively difficult to directly construct a three-dimensional model, and people think that the existing three-dimensional model is modified and edited to obtain a model meeting the requirements. Therefore, a large amount of time can be saved, the efficiency of building and generating the three-dimensional model is improved, and the repeated utilization rate of the three-dimensional model is improved. People hope that the effect of editing the three-dimensional model can be achieved by only needing simple operation of rotating and stretching the model. Therefore, how to effectively and intuitively perform mesh editing processing on a 3D model while maintaining the topology of the original model is one of research contents in digital geometry processing.
Due to the wide application of the three-dimensional grid model, people utilize the existing three-dimensional grid model to generate the three-dimensional grid model meeting the requirements of people, so that certain requirements are provided for the deformation editing of the three-dimensional grid model. People hope to obtain a three-dimensional grid model with better quality through grid deformation editing, simultaneously reduce the complexity of grid model operation, and people can simply and efficiently edit the grid model. Therefore, the criteria for evaluating the grid editing algorithm generally include two points, namely, the quality of the three-dimensional grid model obtained after the three-dimensional grid model is subjected to deformation editing is good, and the complexity of all operations in the process of performing grid deformation editing on the three-dimensional grid model by a user is low.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art, and provides a grid model characteristic editing method based on a compressed manifold mode.
In order to realize the purpose, the technical scheme provided by the invention is as follows: a mesh model characteristic editing method based on a compressed manifold mode comprises the following steps:
1) Obtaining basic parameters of a triangular mesh model;
2) Performing reconstruction calculation on the triangular mesh model by using a compressed manifold mode to obtain a characteristic skeleton model of the triangular mesh model;
3) According to user interaction operation, performing deformation editing on a characteristic skeleton model of the triangular mesh model, and calculating to obtain a skeleton model after deformation editing;
4) Performing skeleton model smoothing processing on the skeleton model after deformation editing through calculation;
5) Adding mesh model details to the feature skeleton model of the deformation editing according to the feature skeleton model constructed by the original mesh model;
6) And repairing the grid model added with the details based on the differential coordinates of the original grid model to obtain the grid model after deformation editing.
In step 1), the basic parameters of the triangular mesh model include the positions of the points, the connection relationship between the points, and the combination of the points formed by each surface.
In step 2), the triangular mesh model reconstructs a characteristic skeleton model by a compressed manifold model, and a characteristic skeleton model of the mesh model is constructed by calculating the first M characteristic vectors of a compressed manifold base of an original mesh model M with n vertexes, wherein the formula is as follows:
in the formula, alpha x ,α y ,α z To represent the eigenvalues of the compressed manifold base of the original mesh model M. Phi is a unit of 1 ,...,φ n To represent the feature vectors of the compressed manifold bases of the original mesh model M.
Respectively representing the values of the x, y, z coordinates of the mesh model reconstructed from the m eigenvectors. Constructing a characteristic skeleton model of the grid, wherein the first m characteristic vectors need to be taken, and m is smaller than n; wherein phi is 1 ,...,φ m Is the first M feature vectors of M, then n i ＝{f x (v i ),f y (v i ),f z (v i ) Formula vertex n i By vertex v i Passing function
For i =1, \8230;, n; grid model S = S constructed by m eigenvectors m Connectivity is the same as that of the mesh model M, called the mesh model S m Is a characteristic skeleton model of the original grid model M.
In step 3), the user selects a region of the mesh that is desired to be deformed, i.e. a region of interest ROI, VROI representing the set of vertices in this region, the user specifies the type of transformation, which can be either a translation type or a rotation type, required for the region of interest, and then the user indicates the target configuration by dragging a certain point to the target position.
In step 4), the mesh model feature skeleton model is smoothed by constructing a minimized energy function E using the first m low-frequency feature functions of the mesh model, and the formula is as follows:
wherein E is an energy function. A. The j Is formed by
The composite material is formed by reconstruction,
is composed of
The characteristic value of (2).
Is a model
The upper vertex. Phi is a j [i]Is the vertex v i J-th characteristic function phi of j Value of (phi) j For the first j low frequency feature functions of the mesh model,
and (5) the vertices of the grid model characteristic skeleton model after deformation editing. To obtain a smooth deformed skeleton model, we wish to have a smooth deformed skeleton model for each
Finding a smooth approximation
Each vertex
Thereby obtaining an improved deformed skeleton model S * . Function(s)
Where a ∈ { x, y, z } is the function M → R on the input surface M.
In step 5), adding mesh model details to the deformed characteristic skeleton model, comprising the following steps:
5.1 For the vertices v of the original mesh model when creating the original feature skeleton model S i And the vertex n of the characteristic skeleton model S constructed according to the original mesh model i The difference between the two is calculated and stored,
is a given detail vector;
5.2 Add the detail vector to the deformed feature skeleton model.
In step 6), according to the differential coordinates of the original mesh model, the feature skeleton model added with the detail vector maintains the differential coordinates of the original mesh model, and the method comprises the following steps:
6.1 Computing differential coordinates of the original mesh model;
6.2 Add detail vectors according to the differential coordinates of the original model M and the smoothly deformed feature skeleton model
And solving the vertex coordinate V' of the original mesh model after deformation to obtain the edited mesh model.
Compared with the prior art, the invention has the following advantages and beneficial effects;
1. the invention realizes the characteristic skeleton model of the grid model based on the compressed manifold mode for the first time, and the characteristic skeleton model of the grid model keeps the outline characteristics of the original grid model, thereby realizing better selection of the local part of the model. And applying the compression manifold mode with better locality to the deformation editing of the three-dimensional grid model. And researching and improving the recovery of the mesh details in the triangular mesh model mesh editing to design a more efficient and natural mesh model mesh editing effect.
2. And finding out the key parts influencing the mesh editing deformation effect, the calculated amount and the editing efficiency in the existing triangular mesh model mesh editing algorithm. The method comprises the steps of establishing a characteristic skeleton model of a triangular mesh model by researching a compressed manifold mode, carrying out mesh editing on the triangular mesh model, and carrying out in-depth research on how to deform the model without affecting details in the editing process.
3. When a grid model feature skeleton model is constructed, different quantities of feature vectors are used for deformation, so that the change of different scales is caused, the feature skeleton model only created by the first few feature vectors can cause the shape change of the global level, and more feature vectors are needed for capturing the local change. And researching the selection of the feature vectors to construct a better feature skeleton model meeting the grid editing requirement.
4. During grid editing, the interested area is translated and rotated, so that discontinuity exists at the boundary of the interested area, and how to perform better smoothing on the boundary after the characteristic skeleton model is deformed is researched.
5. Because only a small amount of feature vectors are used for constructing the feature skeleton model of the model, the deformed feature skeleton model lacks details of the original grid model, and how to add the details of the original model to the feature skeleton model constructed according to the original grid model is researched to obtain the edited grid model.
6. In the process of recovering details, the invention adopts the differential coordinates for keeping the original grid model for the first time, so that the edited grid model is more real.
7. The method has the advantages of wide use space, simple operation and strong adaptability in grid editing.
Drawings
FIG. 1 is a logic flow diagram of the present invention.
FIG. 2 is a continuous structure diagram of the grid model of the present invention.
Fig. 3 is a schematic diagram illustrating a deformation of the selected editing area.
FIG. 4 is a schematic diagram of a deformation of a characteristic skeletal model.
FIG. 5 is a schematic diagram of feature skeleton model deformation smoothing.
Detailed Description
The present invention will be further described with reference to the following specific examples.
As shown in fig. 1 and fig. 2, the specific conditions of the method for editing mesh model features based on compressed manifold mode provided in this embodiment are as follows:
first, a feature skeleton model of the mesh model is constructed
The compressed manifold pattern feature function forms a basis for the squared integrable function defined on the original mesh model M. Similar to the fourier harmonics of the function on the surface, the compressed manifold mode feature function with lower eigenvalues corresponds to the low frequency mode, while the compressed manifold mode feature function with higher eigenvalues corresponds to the details of the high frequency mode description input manifold M. The input is a triangular mesh approximating the hidden surface M. In this case we need a discrete form of the compressed manifold pattern computed from this mesh. The compressed manifold pattern of the mesh itself and its eigenvalues have been shown to be able to converge to the manifold of the hidden manifold because the mesh better approximates the manifold. Since higher feature functions have higher frequencies and therefore capture less detail, we can truncate the number of feature functions (i.e. use only a small number of feature vectors) to reconstruct the surface to obtain different degrees of detail.
Given a surface mesh with n vertices, also denoted by M, we compute the eigenvectors of the compressed manifold base of M, denoted by φ 1 ,...,φ n To indicate. Constructing a characteristic skeleton model of the grid, and taking only the first m characteristic vectors (m is far smaller than n). This gives us an abstraction of capturing its rough features by the higher-level surface through reconstruction. Specifically, let N = { N = 1 ,n 2 ,…,n n Is to use only the first m eigenvectors phi 1 ,...,φ m Vertex set V = { V) from M 1 ,v 2 ,…,v n The set of reconstructed points. Wherein phi 1 ,...,φ m Is the first M feature vectors of M.
Then n i ＝{f x (v i ),f x (v i ),f x (v i ) For i =1, \8230 }, n. Grid model S = S constructed by m eigenvectors m With its apex at n i Connectivity is the same as for mesh model M. We call this mesh model S m Is a characteristic skeleton model of the original grid model M.
Finally, since higher feature functions have higher frequencies and therefore capture less detail, we can truncate the number of feature functions (i.e. use only the fewest coordinate weights) to reconstruct the surface to obtain different degrees of detail.
For how the grid model constructs the feature skeleton model of the grid model, the selection of the number of feature vectors is important. Typically different feature vectors will capture different scales of detail. Thus, warping with different numbers of feature vectors can cause variations in different scales.
In general, a feature skeleton model created from only the first few feature vectors will result in a change in the shape of the mesh model as a whole. To capture local changes, we need more feature vectors. In particular, if the region of interest R is small, we need more feature vectors to build the skeletal model, so that R is reasonably reconstructed in this skeletal model and the corresponding change in coordinate weights is sufficient to deform R. If we select too few feature vectors, the feature skeleton model of the ear is almost collapsed a little, and the ear cannot be represented at all. Since the deformation is calculated for the characteristic skeletal model, the deformation of the ear cannot be described with such a skeletal model. Using more feature vectors, we can capture the ear in the skeleton and further deform.
On the other hand, if the region of interest is large, it is often necessary to make changes over a large range. If we now select too many feature vectors, minimizing the energy function in step 2 attempts to preserve the local details of the feature skeleton model (since there are more terms, i.e., A with a large j j Tracing, drawingThe above-mentioned ones). Roughly speaking, the optimization of the weights of the lower feature vectors is overwhelmed by the large number of higher feature vectors. Thus, the deformation of the feature skeletal model returned in step 2 tends to have some dramatic changes at several points in an attempt to preserve local detail elsewhere. Therefore, in a large range, we need to select a small number of feature vectors to construct a feature skeleton model, so as to emphasize the weight of global deformation.
In summary, the number of feature vectors used for reconstructing the feature skeleton model should be selected according to the size of the region of interest R.
Second step, guess the deformed grid model
First, the user selects the grid area that he wishes to deform. We refer to the region of interest ROI, VROI represents the set of vertices in this region. Next, the user specifies the type of transformation required for the region of interest, which may be a translation type or a rotation type. The user then indicates the target configuration by simply dragging a certain point (e.g., v belongs to VROI) to the target location.
From the transform type of the position combination of the v-sum, if the desired transform is of the translation type, our algorithm computes the translation vector
Or the rotation axis p and the rotation matrix r, if the desired transformation is of the rotation type, we compute the coarse target configuration of the characteristic skeletal model S using the following simple procedure
For all points v i Not belonging to VR, corresponding to point p i The target location of (a) is simply in the feature skeleton model
For each point v i Belonging to VR, if the type of transformation is translation, the target position is
If the transformation type is rotation, then the target position is
In other words, we simply crop the region of interest and apply it to the user-indicated object transform, while the rest of the shape remains unchanged. Such a preliminary guess of the target configuration is of course unsatisfactory. In fact, the deformation is not continuous (along the border of the region of influence R there is a significant, discontinuous variation of the deformation). In the second step, we will see later that our algorithm takes this initial target configuration and produces a better, smoothly curved feature skeleton model. Taking fig. 3 as an example: to bend the body of the dragon, we assign a rotation in the back half of the dragon. We then apply this rotation to the entire region of interest in the feature skeleton model, obtaining a deformed mesh model.
Note that the translational and rotational motion are only the motion modes specified to produce the final deformation in step 2. The resulting deformation is not necessarily rigid.
Thirdly, deforming the characteristic skeleton model of the grid model
For the feature skeleton model S, we have a guessed deformed object model. In this step, we want to guess the deformed object model
To calculate an improved target deformation feature skeleton model S * . In step 3, described in the next section, we will go to S * Adding details to obtain a deformed surface M of an input surface M * 。
Figure 2 illustrates a continuous structure.
To this end, it is noted that each f forms the basis of a family of squared multiplicative functions on M, since the eigenfunctions of M form the basis of a family of squared multiplicative functions on M a Can be written as all characteristic functions phi i s, and furthermore, the low eigenvalue eigenfunction resembles the low frequency mode, while the high eigenvalue eigenfunction corresponds to the high frequency mode. Since our goal is to obtain f a So we ignore high frequency modes. Therefore, we find that only the first m low-frequency feature functions φ are used 1 ,…,φ m . Wherein
We wish to find weights that minimize the energy function
Wherein phi j [i]Is the vertex v i J-th characteristic function phi of j The value of (c):
intuitively, guessing a deformed object model
Requires a high frequency feature function to reconstruct it, and using only low frequency modes results in smoother
This leads to a better deformed skeleton model S. Referring to FIG. 4, the new coordinate weights are derived after step 2
The reconstructed skeletal model shows a smooth transition from the region of interest to the rest.
Deformed characteristic skeleton model
Requires a high frequency feature function to reconstruct it, and using only low frequency modes results in smoother
This leads to a better deformed skeleton model S. Referring to FIG. 5, from the new coordinate weights
The reconstructed skeletal model shows a smooth transition from the region of interest to the rest.
Can be used forThe quantity function E performs a minimization process. There are 3m variables in the energy function in equation (2). To minimize E, we calculate it for A k Gradient of (2)
Wherein
Is a coordinate function that guesses the target skeletal model. We now get all A k The partial derivative of (c) is zero. This results in a system of linear equations of the form: phi A * = b, where Φ is an m × m matrix, Φ i,j ＝<φ i ·φ j > 2 ，A * Is a matrix of m x 3, and,
and b is also an m x 3 matrix, where the i-th behavior
Using A * As coordinate weights, we reconstruct a new deformation feature skeleton model S * 。
Fourthly, restoring the details of the grid model
We now have a feature skeleton model S after deformation smoothing * . Since we construct and deform the feature skeleton model of the original mesh model using only the first few feature vectors, this feature skeleton model lacks the detailed features of the original mesh model. We need to add details to the feature skeleton model S by computation * To obtain a deformed mesh M * . To track the shape details of the mesh model, when creating the original feature skeleton model S, we apply to the vertices v of the original mesh model i And the vertex n of the characteristic skeleton model S constructed according to the original mesh model i The difference between them is calculated and stored. We call it as
The detail vectors are given.
Fifthly, restoring the details of the grid model based on the differential coordinates
When a grid model is added
And then, the part of the grid model is stretched to cause the model to deform, and the deformed grid model can be restored by keeping the differential coordinates of the original grid model to obtain the deformed grid model.
From the Laplacian matrix, by converting the matrix, the following equation can be obtained:
LV'＝δ
in the equation, L is a Laplacian matrix of a vertex coordinate of the original mesh model, V' is a deformed vertex coordinate, and delta is a differential coordinate of the vertex serving as a coordinate.
Adding according to the differential coordinates of the original model M and the smoothly deformed characteristic skeleton model
And solving the vertex coordinate V' of the original mesh model after deformation. By observing this transformation matrix, it is found to be singular, i.e. the rank of the matrix is less than n (number of vertices), which is n-k, where k is the number of connected components in the original model M.
In order to have a solution for the whole linear system, resulting in the vertex coordinates V' of the final deformation, we need cartesian coordinates of at least one vertex to determine the spatial position, assuming M is connected, i.e. k is 1. And the vertex coordinates are used as constraint conditions in an equation to enable a linear system to have a solution, and are used as constraint points in space to control the deformation of the whole mesh model. Let the set of known constraint points be C, i.e. there are additional constraints:
v j ＝c j ,j∈C
let C = {1,2, ·, m }, i.e., there are m control points, the following system of linear equations can be obtained:
and after a new conversion matrix is obtained, adding a constant term to the index value of the constraint vertex at the corresponding position on the right side of the equation. Therefore, the whole linear system is in a column full rank, and the final result is obtained by solving through the least square principle.
Least squares (also known as the least squares method) is a mathematical optimization technique. It matches by minimizing the sum of the squares of the errors to find the best function of the data. The unknown data can be easily obtained by the least square method, and the sum of squares of errors between the obtained data and actual data is minimized. According to the least square principle, the energy change on the control point is actually uniformly diffused to the whole grid, and finally the deformation model keeping the differential coordinate of the original grid model is obtained.
Expressed by an optimized equation:
let us note that the L matrix after adding the information of the known m constraint points is L ', and the δ on the right side is also correspondingly expanded to δ ', then the formula LV ' = δ becomes:
L'V'＝δ'
the L ' row is (m + n) multiplied by n, the L ' row is n multiplied by 3, the delta ' row is (m + n) multiplied by 3, the least square method is applied, two sides of the equation are simultaneously multiplied by the transposition matrix (L ') of L ') T ：
(((L') T )L')V'＝((L') T )δ'
Along with the great acceleration of the solving speed of the large sparse matrix, the efficiency of the whole deformation grid model detail adding process is improved, and finally the deformation edited grid model is obtained.
The above-mentioned embodiments are only preferred embodiments of the present invention, and the scope of the present invention is not limited thereby, and all changes made in the shape and principle of the present invention should be covered within the scope of the present invention.
Claims (6)
1. A mesh model characteristic editing method based on a compressed manifold mode is characterized by comprising the following steps:
1) Acquiring basic parameters of a triangular mesh model;
2) Performing reconstruction calculation on the triangular mesh model by using a compressed manifold mode to obtain a characteristic skeleton model of the triangular mesh model;
the triangular mesh model reconstructs a characteristic skeleton model through a compressed manifold model, and a characteristic skeleton model of the mesh model is constructed by calculating the first M characteristic vectors of a compressed manifold base of an original mesh model M with n vertexes, wherein the formula is as follows:
in the formula, alpha x ,α y ,α z To represent the eigenvalues of the compressed manifold base of the original mesh model M; phi is a unit of 1 ,...,φ n To represent the feature vectors of the compressed manifold base of the original mesh model M;
respectively representing the values of x, y and z coordinates of the grid model reconstructed according to the m characteristic value characteristic vectors; constructing a characteristic skeleton model of the grid, wherein the first m characteristic vectors need to be taken, and m is smaller than n; wherein phi 1 ,...,φ m Is the first M feature vectors of M, then n i ＝{f x (v i ),f y (v i ),f z (v i ) }, formulating vertex n i By vertex v i Passing function
The conversion yields, for i =1, \8230;, n; grid model S = S constructed by m eigenvectors m Connectivity is the same as that of the mesh model M, called the mesh model S m A characteristic skeleton model of the original grid model M;
3) According to user interaction operation, performing deformation editing on a characteristic skeleton model of the triangular mesh model, and calculating to obtain a skeleton model after deformation editing;
4) Performing skeleton model smoothing treatment on the skeleton model subjected to deformation editing through calculation;
5) Adding mesh model details to the feature skeleton model edited by deformation according to the feature skeleton model constructed by the original mesh model;
6) And repairing the grid model added with the details based on the differential coordinates of the original grid model to obtain the grid model after deformation editing.
2. The method for editing characteristics of a mesh model based on a compressed manifold mode as claimed in claim 1, wherein: in step 1), the basic parameters of the triangular mesh model include the positions of the points, the connection relationship between the points and the combination of the points formed by each surface.
3. The method for editing characteristics of a mesh model based on a compressed manifold mode as claimed in claim 1, wherein: in step 3), the user selects a region of the mesh that is desired to be deformed, i.e. a region of interest ROI, VROI representing the set of vertices in this region, the user specifies the type of transformation, which can be either a translation type or a rotation type, required for the region of interest, and then the user indicates the target configuration by dragging a certain point to the target position.
4. The method for editing characteristics of a mesh model based on a compressed manifold mode as claimed in claim 1, wherein: in step 4), the minimum energy function E is constructed by using the first m low-frequency feature functions of the grid model, and the grid model feature skeleton model is smoothed by the following formula:
wherein E is an energy function; a. The j Is composed of
The medicine is prepared by the reconstruction of the medicine,
is composed of
A characteristic value of (d);
is a model
An upper vertex; phi is a j [i]Is the vertex v i J-th characteristic function phi of j Value of (phi) j For the first j low frequency feature functions of the mesh model,
the vertex of the grid model characteristic skeleton model after deformation editing; to obtain a smooth deformed skeleton model, it is desirable to have each one
Finding a smooth approximation
Each vertex
Thereby obtaining an improved deformed skeleton model S * (ii) a Function(s)
Where a ∈ { x, y, z } is the function M → R on the input surface M.
5. The method for editing characteristics of a mesh model based on a compressed manifold mode as claimed in claim 1, wherein: in step 5), adding mesh model details to the deformed characteristic skeleton model, comprising the following steps:
5.1 For the vertices v of the original mesh model when creating the original feature skeleton model S i And the vertex n of the characteristic skeleton model S constructed according to the original mesh model i The difference between the two is calculated and stored,
is a given detail vector;
5.2 Add the detail vector to the deformed feature skeleton model.
6. The method for editing characteristics of a mesh model based on a compressed manifold mode as claimed in claim 1, wherein: in step 6), according to the differential coordinates of the original mesh model, the feature skeleton model added with the detail vector maintains the differential coordinates of the original mesh model, and the method comprises the following steps:
6.1 Computing differential coordinates of the original mesh model;
6.2 Add detail vectors according to differential coordinates of the original model M and the smoothly deformed feature skeleton model
And solving the vertex coordinate V' of the original mesh model after deformation to obtain the edited mesh model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910135990.2A CN109801367B (en) | 2019-02-25 | 2019-02-25 | Grid model characteristic editing method based on compressed manifold mode |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910135990.2A CN109801367B (en) | 2019-02-25 | 2019-02-25 | Grid model characteristic editing method based on compressed manifold mode |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109801367A CN109801367A (en) | 2019-05-24 |
| CN109801367B true CN109801367B (en) | 2023-01-13 |
Family
ID=66561271
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910135990.2A Active CN109801367B (en) | 2019-02-25 | 2019-02-25 | Grid model characteristic editing method based on compressed manifold mode |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109801367B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112462689B (en) * | 2020-12-10 | 2022-08-02 | 德州钰雕机械设备有限公司 | Method for generating handicraft digital model random carving four-axis three-linkage cutter path |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6452596B1 (en) * | 1998-10-06 | 2002-09-17 | International Business Machines Corporation | Methods and apparatus for the efficient compression of non-manifold polygonal meshes |
| CN101261741A (en) * | 2007-10-19 | 2008-09-10 | 北京航空航天大学 | A physical distortion method based on details coding and reconstruction |
| CN101354788A (en) * | 2008-08-20 | 2009-01-28 | 大连民族学院 | One-dimensional nondestructive geometric compressing method of three-dimensional grid model |
| CN102509356A (en) * | 2011-11-10 | 2012-06-20 | 杭州电子科技大学 | Detail-kept geometrical model deformation method using grid subdivision |
| CN103688548A (en) * | 2011-07-08 | 2014-03-26 | 汤姆逊许可公司 | System and method for encoding and decoding a bitstream for a 3D model having repetitive structure |
| CN107888915A (en) * | 2017-11-07 | 2018-04-06 | 武汉大学 | A kind of perception compression method of combination dictionary learning and image block |
| CN109299569A (en) * | 2018-10-24 | 2019-02-01 | 广州市香港科大霍英东研究院 | A kind of Large eddy simulation method of the incompressible viscous flows body based on coherent structure |
-
2019
- 2019-02-25 CN CN201910135990.2A patent/CN109801367B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6452596B1 (en) * | 1998-10-06 | 2002-09-17 | International Business Machines Corporation | Methods and apparatus for the efficient compression of non-manifold polygonal meshes |
| CN101261741A (en) * | 2007-10-19 | 2008-09-10 | 北京航空航天大学 | A physical distortion method based on details coding and reconstruction |
| CN101354788A (en) * | 2008-08-20 | 2009-01-28 | 大连民族学院 | One-dimensional nondestructive geometric compressing method of three-dimensional grid model |
| CN103688548A (en) * | 2011-07-08 | 2014-03-26 | 汤姆逊许可公司 | System and method for encoding and decoding a bitstream for a 3D model having repetitive structure |
| CN102509356A (en) * | 2011-11-10 | 2012-06-20 | 杭州电子科技大学 | Detail-kept geometrical model deformation method using grid subdivision |
| CN107888915A (en) * | 2017-11-07 | 2018-04-06 | 武汉大学 | A kind of perception compression method of combination dictionary learning and image block |
| CN109299569A (en) * | 2018-10-24 | 2019-02-01 | 广州市香港科大霍英东研究院 | A kind of Large eddy simulation method of the incompressible viscous flows body based on coherent structure |
Non-Patent Citations (1)
| Title |
|---|
| 基于小波和偶合特征的多数据流压缩算法;陈安龙 等;《Journal of Software》;20070228;第18卷(第2期);第177-184页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109801367A (en) | 2019-05-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109147048B (en) | Three-dimensional mesh reconstruction method by utilizing single-sheet colorful image | |
| Yu et al. | Mesh editing with poisson-based gradient field manipulation | |
| Schmidt et al. | Interactive decal compositing with discrete exponential maps | |
| Ji et al. | B‐Mesh: a modeling system for base meshes of 3D articulated shapes | |
| Wesche et al. | Freedrawer: a free-form sketching system on the responsive workbench | |
| Kho et al. | Sketching mesh deformations | |
| US6307554B1 (en) | Apparatus and method for generating progressive polygon data, and apparatus and method for generating three-dimensional real-time graphics using the same | |
| Xu et al. | Poisson shape interpolation | |
| Shi et al. | A fast multigrid algorithm for mesh deformation | |
| Hornung et al. | Character animation from 2d pictures and 3d motion data | |
| Zhou et al. | 3D surface filtering using spherical harmonics | |
| Zhang et al. | Bas-relief generation and shape editing through gradient-based mesh deformation | |
| CN114429538B (en) | Method for interactively editing nerve radiation field geometry | |
| CN111127658A (en) | Point cloud reconstruction-based feature-preserving curved surface reconstruction method for triangular mesh curved surface | |
| CA2396419C (en) | System and method for multi-resolution fairing of non-manifold models | |
| CN109801367B (en) | Grid model characteristic editing method based on compressed manifold mode | |
| Dey et al. | Eigen deformation of 3d models | |
| Yang et al. | 3D shape metamorphosis based on T-spline level sets | |
| CN115222894A (en) | Image topological deformation method based on laplace optimization | |
| CN108876922A (en) | A kind of mesh amending method based on the regularization of interior dihedral angle supplementary angle | |
| Agarwal et al. | GAMesh: Guided and augmented meshing for deep point networks | |
| Eyiyurekli et al. | Detail-preserving level set surface editing and geometric texture transfer | |
| Xu et al. | Free-form deformation with rational DMS-spline volumes | |
| CN117437362B (en) | Three-dimensional animation model generation method and system | |
| CN103460252B (en) | A method for reconstructing a 3d surface from a 2d sketch |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |