Gang Dang

Technical Section: Robust normal estimation for point clouds with sharp features

This paper presents a novel technique for estimating normals on unorganized point clouds. Methods from robust statistics are used to detect the best local tangent plane for each point. Therefore the algorithm is capable to deal with points located in high curvature regions or near/on complex sharp features, while being highly robust with respect to noise and outliers. In particular, the presented method reliably recovers sharp features but does not require tedious manual parameter tuning as done by current methods. The key ingredients of our approach are a robust noise-scale estimator and a kernel density estimation (KDE) based objective function. In contrast to previous approaches the noise-scale estimation is not affected by sharp features and achieves high accuracy even in the presence of outliers. In addition, our normal estimation procedure allows detection and elimination of outliers. We confirm the validity and reliability of our approach on synthetic and measured data and demonstrate applications to point cloud denoising.

A survey of methods for moving least squares surfaces

Moving least squares (MLS) surfaces representation directly defines smooth surfaces from point cloud data, on which the differential geometric properties of point set can be conveniently estimated. Nowadays, the MLS surfaces have been widely applied in the processing and rendering of point-sampled models and increasingly adopted as the standard definition of point set surfaces. We classify the MLS surface algorithms into two types: projection MLS surfaces and implicit MLS surfaces, according to employing a stationary projection or a scalar field in their definitions. Then, the properties and constrains of the MLS surfaces are analyzed. After presenting its applications, we summarize the MLS surfaces definitions in a generic form and give the outlook of the future work at last.

Curve skeleton extraction by coupled graph contraction and surface clustering

In this paper, we present a practical algorithm to extract a curve skeleton of a 3D shape. The core of our algorithm comprises coupled processes of graph contraction and surface clustering. Given a 3D shape represented by a triangular mesh, we first construct an initial skeleton graph by directly copying the connectivity and geometry information from the input mesh. Graph contraction and surface clustering are then performed iteratively. The former merges certain graph nodes based on computation of an approximate centroidal Voronoi diagram, seeded by subsampling the graph nodes from the previous iteration. Meanwhile, a coupled surface clustering process serves to regularize the graph contraction. Constraints are used to ensure that extremities of the graph are not shortened undesirably, to ensure that skeleton has the correct topological structure, and that surface clustering leads to an approximately-centered skeleton of the input shape. These properties lead to a stable and reliable skeleton graph construction algorithm. Experiments demonstrate that our skeleton extraction algorithm satisfies various desirable criteria. Firstly, it produces a skeleton homotopic with the input (the genus of both shapes agree) which is both robust (results are stable with respect to noise and remeshing of the input shape) and reliable (every boundary point is visible from at least one curve-skeleton location). It can also handle point cloud data if we first build an initial skeleton graph based on k-nearest neighbors. In addition, a secondary output of our algorithm is a skeleton-to-surface mapping, which can e.g. be used directly for skinning animation. Highlights: (1) An algorithm for curve skeleton extraction from 3D shapes based on coupled graph contraction and surface clustering. (2) The algorithm meets various desirable criteria and can be extended to work for incomplete point clouds.

Non-rigid Registration in 3D Implicit Vector Space

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.

Realtime Reconstruction of an Animating Human Body from a Single Depth Camera

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.

A mesh meaningful segmentation algorithm using Skeleton and minima-rule

In this paper, a hierarchical shape decomposition algorithm is proposed, which integrates the advantages of skeleton-based and minima-rule-based meaningful segmentation algorithms. The method makes use of new geometrical and topological functions of skeleton to define initial cutting critical points, and then employs salient contours with negative minimal principal curvature values to determine natural final boundary curves among parts. And sufficient experiments have been carried out on many meshes, and shown that our framework can provide more reasonable perceptual results than single skeleton-based [8] or minima-rule-based [15] algorithm. In addition, our algorithm not only can divide a mesh of any genus into a collection of genus zero, but also partition level-of-detail meshes into similar parts.

Curve skeleton extraction by graph contraction

In this paper, we propose a practical algorithm for extracting curve skeletons from a 3D shape represented by a triangular mesh. We first construct an initial skeleton graph by copying the connectivity and geometry information from the input mesh. We then perform iterative skeletonization over the nodes of the skeleton graph using coupled processes of graph contraction and surface clustering. In the contraction step, the skeleton graph is simplified and regularized with surface clustering: mesh vertices are clustered, while the positions of nodes in the skeleton graph are updated at the same time. Eventually, the skeleton graph is automatically simplified to an approximately-centered curve skeleton. Our algorithm naturally produces a skeleton-to-surface mapping, making the output skeletons directly applicable to skinning deformation.

A service-oriented architecture for tele-immersion

Sharing virtual environments with the remote collaborators as well as the topic of the collaboration may be better than collaborating with them in person. In this paper we will discuss our ongoing work to enable effective collaboration between remote participants within tele-immersion based on Web service implemented by Microsoft Visual Studio .Net. We will discuss the architecture of the tele-immersion system created, the implemented methodology, the technology driving our current research, and the lessons learned. Our focus is on the service-oriented architecture taking of the Web service and COM+ technology.

Feature preserving consolidation for unorganized point clouds

We introduce a novel method for the consolidation of unorganized point clouds with noise, outliers, non-uniformities as well as sharp features. This method is feature preserving, in the sense that given an initial estimation of normal, it is able to recover the sharp features contained in the original geometric data which are usually contaminated during the acquisition. The key ingredient of our approach is a weighting term from normal space as an effective complement to the recently proposed consolidation techniques. Moreover, a normal mollification step is employed during the consolidation to get normal information respecting sharp features besides the position of each point. Experiments on both synthetic and real-world scanned models validate the ability of our approach in producing denoised, evenly distributed and feature preserving point clouds, which are preferred by most surface reconstruction methods.

An adaptive octree textures painting algorithm

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.