Canonical forms attempt to factor out a non-rigid shape’s pose, giving a pose-neutral shape. This opens up the possibility of using methods originally designed for rigid shape retrieval for the task of non-rigid shape retrieval. We extend our recent benchmark for testing canonical form algorithms. Our new benchmark is used to evaluate a greater number of state-of-the-art canonical forms, on five recent non-rigid retrieval datasets, within two different retrieval frameworks. A total of fifteen different canonical form methods are compared. We find that the difference in retrieval accuracy between different canonical form methods is small, but varies significantly across different datasets. We also find that efficiency is the main difference between the methods.

Shape registration is fundamental to 3D object acquisition; it is used to fuse scans from multiple views. Existing algorithms mainly utilize geometric information to determine alignment, but this typically results in noticeable misalignment of textures (i.e. surface colors) when using RGB-depth cameras. We address this problem using a novel approach to color-aware registration, which takes both color and geometry into consideration simultaneously. Color information is exploited throughout the pipeline to provide more effective sampling, correspondence and alignment, in particular for surfaces with detailed textures. Our method can furthermore tackle both rigid and non-rigid registration problems (arising, for example, due to small changes in the object during scanning, or camera distortions). We demonstrate that our approach produces significantly better results than previous methods.

We present a method for realtime reconstruction of an animating human body,which produces a sequence of deforming meshes representing a given performance captured by a single commodity depth camera. We achieve realtime single-view mesh completion by enhancing the parameterized SCAPE model.Our method, which we call Realtime SCAPE, performs full-body reconstruction without the use of markers.In Realtime SCAPE, estimations of body shape parameters and pose parameters, needed for reconstruction, are decoupled. Intrinsic body shape is first precomputed for a given subject, by determining shape parameters with the aid of a body shape database. Subsequently, per-frame pose parameter estimation is performed by means of linear blending skinning (LBS); the problem is decomposed into separately finding skinning weights and transformations. The skinning weights are also determined offline from the body shape database,reducing online reconstruction to simply finding the transformations in LBS. Doing so is formulated as a linear variational problem;carefully designed constraints are used to impose temporal coherence and alleviate artifacts. Experiments demonstrate that our method can produce full-body mesh sequences with high fidelity.

In this paper, we propose a data-driven approach for realistic deformation of articulated meshes: the wide availability of pose data makes such an approach feasible. We use multiple reference models, combined with a fast linear blending skinning (LBS) method in local spaces. By exploiting information from the reference models, realistic deformation is achieved, yet with computational efficiency comparable to traditional blending methods-our approach is suitable for real-time applications. We demonstrate the effectiveness of our algorithm using various examples.

Feature matching is a challenging problem at the heart of numerous computer graphics and computer vision applications. We present the SuperMatching algorithm for finding correspondences between two sets of features. It does so by considering triples or higher order tuples of points, going beyond the pointwise and pairwise approaches typically used. SuperMatching is formulated using a supersymmetric tensor representing an affinity metric that takes into account feature similarity and geometric constraints between features: Feature matching is cast as a higher order graph matching problem. SuperMatching takes advantage of supersymmetry to devise an efficient sampling strategy to estimate the affinity tensor, as well as to store the estimated tensor compactly. Matching is performed by an efficient higher order power iteration approach that takes advantage of this compact representation. Experiments on both synthetic and real data show that SuperMatching provides more accurate feature matching than other state-of-the-art approaches for a wide range of 2D and 3D features, with competitive computational cost.

Three-dimensional surface registration transforms multiple three-dimensional data sets into the same coordinate system so as to align overlapping components of these sets. Recent surveys have covered different aspects of either rigid or nonrigid registration, but seldom discuss them as a whole. Our study serves two purposes: 1) To give a comprehensive survey of both types of registration, focusing on three-dimensional point clouds and meshes and 2) to provide a better understanding of registration from the perspective of data fitting. Registration is closely related to data fitting in which it comprises three core interwoven components: model selection, correspondences and constraints, and optimization. Study of these components 1) provides a basis for comparison of the novelties of different techniques, 2) reveals the similarity of rigid and nonrigid registration in terms of problem representations, and 3) shows how overfitting arises in nonrigid registration and the reasons for increasing interest in intrinsic techniques. We further summarize some practical issues of registration which include initializations and evaluations, and discuss some of our own observations, insights and foreseeable research trends.

We present an algorithm for multi-scale partial intrinsic symmetry detection over 2D and 3D shapes, where the scale of a symmetric region is defined by intrinsic distances between symmetric points over the region. To identify prominent symmetric regions which overlap and vary in form and scale, we decouple scale extraction and symmetry extraction by performing two levels of clustering. First, significant symmetry scales are identified by clustering sample point pairs from an input shape. Since different point pairs can share a common point, shape regions covered by points in different scale clusters can overlap. We introduce the symmetry scale matrix (SSM), where each entry estimates the likelihood two point pairs belong to symmetries at the same scale. The pair-to-pair symmetry affinity is computed based on a pair signature which encodes scales. We perform spectral clustering using the SSM to obtain the scale clusters. Then for all points belonging to the same scale cluster, we perform the second-level spectral clustering, based on a novel point-to-point symmetry affinity measure, to extract partial symmetries at that scale. We demonstrate our algorithm on complex shapes possessing rich symmetries at multiple scales.

Abstract We introduce symmetry hierarchy of man‐made objects, a high‐level structural representation of a 3D model providing a symmetry‐induced, hierarchical organization of the model's constituent parts. Given an input mesh, we segment it into primitive parts and build an initial graph which encodes inter‐part symmetries and connectivity relations, as well as self‐symmetries in individual parts. The symmetry hierarchy is constructed from the initial graph via recursive graph contraction which either groups parts by symmetry or assembles connected sets of parts. The order of graph contraction is dictated by a set of precedence rules designed primarily to respect the law of symmetry in perceptual grouping and the principle of compactness of representation. We show that symmetry hierarchy naturally implies a hierarchical segmentation that is more meaningful than those produced by local geometric considerations. We also develop an application of symmetry hierarchies for structural shape editing.

We present an implicit approach for pair-wise non-rigid registration of moving and deforming objects. Shapes of interest are implicitly embedded in the 3D implicit vector space. In this implicit embedding space, registration is performed using a global-to-local framework. Firstly, a non-linear optimization functional defined on the vector distance function is used to find the global alignment between shapes. Secondly, an incremental cubic B-spline free form deformation is used to recover the non-rigid transformation parameters. Local non-rigid registration is posed in terms of minimising an energy functional, for which we give a closed-form linear system and solve it using an improved iterative Gauss-Seidel method. Our approach can consistently produce smooth and continuous registration fields, and correctly establish dense one-to-one correspondences. It can naturally deal with both open partial and closed shapes, and imperfect models with gaps and noise, through its use of the implicit vector representation. Experimental results on several datasets demonstrate the robustness of the proposed method.

We perform co-analysis of a set of man-made 3D objects to allow the creation of novel instances derived from the set. We analyze the objects at the part level and treat the anisotropic part scales as a shape style. The co-analysis then allows style transfer to synthesize new objects. The key to co-analysis is part correspondence, where a major challenge is the handling of large style variations and diverse geometric content in the shape set. We propose style-content separation as a means to address this challenge. Specifically, we define a correspondence-free style signature for style clustering. We show that confining analysis to within a style cluster facilitates tasks such as co-segmentation, content classification, and deformation-driven part correspondence. With part correspondence between each pair of shapes in the set, style transfer can be easily performed. We demonstrate our analysis and synthesis results on several sets of man-made objects with style and content variations.

Traditional Texturing using a set of two dimensional image maps is an established and widespread practice. However, it is difficult to parameterize a model in texture space, particularly with representations such as implicit surfaces, subdivision surfaces, and very dense or detailed polygonal meshes. Based on an adaptive octree textures definition, this paper proposes a direct reverse-projecting pixel-level painting approach which has less storage requirements to general octree textures maps. In addition, it depends on texture lookup in the GPU, which particularly lookup faster than the Non-GPU program.

Decision trees have been widely used for online learning classification. Many approaches usually need large data stream to finish decision trees induction, as show notable limitations (even fail) with small data stream. In fact, there exist many real instances with small data stream. In the paper, we propose a novel incremental extremely random forest algorithm, dealing with online learning classification with small streaming data. In our method, arriving examples are stored at the leaf nodes and used to determine when to split the leaf nodes combined with Gini index, so the trees can be expanded efficiently with a few examples. Our algorithm has been applied to solve both online learning and video object tracking problems, and the results on UCI datasets and challenging video sequences demonstrate its effectiveness and robustness.

Traditional texturing using a set of two dimensional image maps is an established and widespread practice. However, it is difficult to parameterize a model in texture space, particularly with representations such as implicit surfaces, subdivision surfaces, and very dense or detailed polygonal meshes. Based on an adaptive octree textures definition, this paper proposes a direct reverse-projecting pixel-level painting approach which has less storage requirements to general octree textures maps. In addition, it depends on texture lookup in the GPU, which particularly lookup faster than the non-GPU program.

The effort of on-the-fly accessing 3D contents over the Internet has been done in recent years. And 3D streaming has been investigated to represent 3D models in the compact format and progressively transmit them on the limited-bandwidth and lossy channel. In the paper, an error resilient 3D mesh coding algorithm is presented, which employs an extended multiple quantization (EMQ) arithmetic coder method, inspired by the error-resilient JPEG 2000 image coding standards. With periodic arithmetic coder restarting and termination markers, the error resilient EMQ coder divides bit stream into little independent parts and enables basic transmission error containment. Furthermore, the EMQ coder has the intrinsic capacity of handling noise by using the maximum a posteriori (MAP) decoder. Experiments show that the method improves the mesh transmission quality in a simulated network environment.

Abstract In this paper, we propose a fast and stable method for 2D shape deformation based on rigid square matching. Our method utilizes uniform quadrangular control meshes for 2D shapes and tries to maintain the rigidity of each square in the control mesh during user manipulation. A rigid shape matching method is performed to find an optimal pure rotational transformation for each square in the control mesh. An iterative solver is proposed to compute the final deformation result for the entire control mesh by minimizing the difference between the deformed vertices and their counterparts in the neighboring rigid square. The deformation result on the 2D shape is as rigid as possible and the details of the shape are preserved well. As extensions, we present a shape‐aware splitting method to improve the deformation effect for coarse meshes and a simple sketch‐based clustering method for skeletal deformation. Experiments with various 2D shapes show that our method is efficient and easy to use, and can provide physically plausible result for shapes of objects in real world. Therefore, our shape deformation method is especially suitable for applications in cartoon character animation. Copyright © 2008 John Wiley & Sons, Ltd.

On the basis of minima rule from the cognitive theory, this paper presents an algorithm decomposing a mesh into smaller parts by feature contours, gotten from the minima negative curvature vertices. The algorithm is carried out in two steps. Firstly, to avoid over-segmentation, our method excludes unimportant local adjacent similar contours. Secondly, the remnant salient contours are automatically completed to form short loops around mesh's parts, constrained by two near parallel cutting planes that are determined by principal component analysis of all vertices. The algorithm has been demonstrated on many meshes, and the results show that it not only can perceptual group the adjacent self-similarity regions, but also can achieve reasonable segmentations.