Feature-sensitive and Adaptive Image Triangulation: A Super-pixel-based Scheme for Image Segmentation and Mesh Generation

With increasing utilization of various imaging techniques (such as CT, MRI and PET) in medical fields, it is often in great need to computationally extract the boundaries of objects of interest, a process commonly known as image segmentation. While numerous approaches have been proposed in literature on automatic/semi-automatic image segmentation, most of these approaches are based on image pixels. The number of pixels in an image can be huge, especially for 3D imaging volumes, which renders the pixel-based image segmentation process inevitably slow. On the other hand, 3D mesh generation from imaging data has become important not only for visualization and quantification but more critically for finite element based numerical simulation. Traditionally image-based mesh generation follows such a procedure as: (1) image boundary segmentation, (2) surface mesh generation from segmented boundaries, and (3) volumetric (e.g., tetrahedral) mesh generation from surface meshes. These three majors steps have been commonly treated as separate algorithms/steps and hence image information, once segmented, is not considered any more in mesh generation. In this thesis, we investigate a super-pixel based scheme that integrates both image segmentation and mesh generation into a single method, making mesh generation truly an image-incorporated approach. Our method, called image content-aware mesh generation, consists of several main steps. First, we generate a set of feature-sensitive, and adaptively distributed points from 2D grayscale images or 3D volumes. A novel image edge enhancement method via randomized shortest paths is introduced to be an optional choice to generate the features’ boundary map in mesh node generation step. Second, a Delaunay-triangulation generator (2D) or tetrahedral mesh generator (3D) is then utilized to generate a 2D triangulation or 3D tetrahedral mesh. The generated triangulation (or tetrahedralization) provides an adaptive partitioning of a given image (or volume). Each cluster of pixels within a triangle (or voxels within a tetrahedron) is called a super-pixel, which forms one of the nodes of a graph and adjacent super-pixels give an edge of the graph. A graph-cut method is then applied to the graph to define the boundary between two subsets of the graph, resulting in good boundary segmentations with high quality meshes. Thanks to the significantly reduced number of elements (super-pixels) as compared to that of pixels in an image, the super-pixel based segmentation method has tremendously improved the segmentation speed, making it feasible for real-time feature detection. In addition, the incorporation of image segmentation into mesh generation makes the generated mesh well adapted to image features, a desired property known as feature-preserving mesh generation

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

3D Shape Segmentation with Projective Convolutional Networks

This paper introduces a deep architecture for segmenting 3D objects into their labeled semantic parts. Our architecture combines image-based Fully Convolutional Networks (FCNs) and surface-based Conditional Random Fields (CRFs) to yield coherent segmentations of 3D shapes. The image-based FCNs are used for efficient view-based reasoning about 3D object parts. Through a special projection layer, FCN outputs are effectively aggregated across multiple views and scales, then are projected onto the 3D object surfaces. Finally, a surface-based CRF combines the projected outputs with geometric consistency cues to yield coherent segmentations. The whole architecture (multi-view FCNs and CRF) is trained end-to-end. Our approach significantly outperforms the existing state-of-the-art methods in the currently largest segmentation benchmark (ShapeNet). Finally, we demonstrate promising segmentation results on noisy 3D shapes acquired from consumer-grade depth cameras.Comment: This is an updated version of our CVPR 2017 paper. We incorporated new experiments that demonstrate ShapePFCN performance under the case of consistent *upright* orientation and an additional input channel in our rendered images for encoding height from the ground plane (upright axis coordinate values). Performance is improved in this settin

Sketching-based Skeleton Extraction

Articulated character animation can be performed by manually creating and rigging a skeleton into an unfolded 3D mesh model. Such tasks are not trivial, as they require a substantial amount of training and practice. Although methods have been proposed to help automatic extraction of skeleton structure, they may not guarantee that the resulting skeleton can help to produce animations according to user manipulation. We present a sketching-based skeleton extraction method to create a user desired skeleton structure which is used in 3D model animation. This method takes user sketching as an input, and based on the mesh segmentation result of a 3D mesh model, generates a skeleton for articulated character animation. In our system, we assume that a user will properly sketch bones by roughly following the mesh model structure. The user is expected to sketch independently on different regions of a mesh model for creating separate bones. For each sketched stroke, we project it into the mesh model so that it becomes the medial axis of its corresponding mesh model region from the current viewer perspective. We call this projected stroke a “sketched bone”. After pre-processing user sketched bones, we cluster them into groups. This process is critical as user sketching can be done from any orientation of a mesh model. To specify the topology feature for different mesh parts, a user can sketch strokes from different orientations of a mesh model, as there may be duplicate strokes from different orientations for the same mesh part. We need a clustering process to merge similar sketched bones into one bone, which we call a “reference bone”. The clustering process is based on three criteria: orientation, overlapping and locality. Given the reference bones as the input, we adopt a mesh segmentation process to assist our skeleton extraction method. To be specific, we apply the reference bones and the seed triangles to segment the input mesh model into meaningful segments using a multiple-region growing mechanism. The seed triangles, which are collected from the reference bones, are used as the initial seeds in the mesh segmentation process. We have designed a new segmentation metric [1] to form a better segmentation criterion. Then we compute the Level Set Diagrams (LSDs) on each mesh part to extract bones and joints. To construct the final skeleton, we connect bones extracted from all mesh parts together into a single structure. There are three major steps involved: optimizing and smoothing bones, generating joints and forming the skeleton structure. After constructing the skeleton model, we have proposed a new method, which utilizes the Linear Blend Skinning (LBS) technique and the Laplacian mesh deformation technique together to perform skeleton-driven animation. Traditional LBS techniques may have self-intersection problem in regions around segmentation boundaries. Laplacian mesh deformation can preserve the local surface details, which can eliminate the self-intersection problem. In this case, we make use of LBS result as the positional constraint to perform a Laplacian mesh deformation. By using the Laplacian mesh deformation method, we maintain the surface details in segmentation boundary regions. This thesis outlines a novel approach to construct a 3D skeleton model interactively, which can also be used in 3D animation and 3D model matching area. The work is motivated by the observation that either most of the existing automatic skeleton extraction methods lack well-positioned joints specification or the manually generated methods require too much professional training to create a good skeleton structure. We dedicate a novel approach to create 3D model skeleton based on user sketching which specifies articulated skeleton with joints. The experimental results show that our method can produce better skeletons in terms of joint positions and topological structure

Point Cloud Structural Parts Extraction based on Segmentation Energy Minimization

In this work we consider 3D point sets, which in a typical setting represent unorganized point clouds. Segmentation of these point sets requires first to single out structural components of the unknown surface discretely approximated by the point cloud. Structural components, in turn, are surface patches approximating unknown parts of elementary geometric structures, such as planes, ellipsoids, spheres and so on. The approach used is based on level set methods computing the moving front of the surface and tracing the interfaces between different parts of it. Level set methods are widely recognized to be one of the most efficient methods to segment both 2D images and 3D medical images. Level set methods for 3D segmentation have recently received an increasing interest. We contribute by proposing a novel approach for raw point sets. Based on the motion and distance functions of the level set we introduce four energy minimization models, which are used for segmentation, by considering an equal number of distance functions specified by geometric features. Finally we evaluate the proposed algorithm on point sets simulating unorganized point clouds