Surface feature based mesh segmentation

Mesh segmentation has a variety of applications in product design, reverse engineering, and rapid prototyping fields. This paper presents a novel algorithm of mesh segmentation from original scanning data points, which essentially consists of three steps. Normal based initial decomposing is first performed to recognize plane features. Then we implement further segmentation based on curvature criteria and Gauss mapping, followed by the detection of quadric surface features. The segmentation refinement is finally achieved using B-spline surface fitting technology. The experimental results on many 3D models have demonstrated the effectiveness and robustness of the proposed segmentation method.     

Hierarchical mesh segmentation based on fitting primitives

In this paper, we describe a hierarchical face clustering algorithm for triangle meshes based on fitting primitives belonging to an arbitrary set. The method proposed is completely automatic, and generates a binary tree of clusters, each of which is fitted by one of the primitives employed. Initially, each triangle represents a single cluster; at every iteration, all the pairs of adjacent clusters are considered, and the one that can be better approximated by one of the primitives forms a new single cluster. The approximation error is evaluated using the same metric for all the primitives, so that it makes sense to choose which is the most suitable primitive to approximate the set of triangles in a cluster. Based on this approach, we have implemented a prototype that uses planes, spheres and cylinders, and have experimented that for meshes made of 100 K faces, the whole binary tree of clusters can be built in about 8 s on a standard PC. The framework described here has natural application in reverse engineering processes, but it has also been tested for surface denoising, feature recovery and character skinning.     

Cropping-resilient 3D mesh watermarking based on consistent segmentation and mesh steganalysis

Multimedia Tools and Applications - This paper presents a new approach to 3D mesh watermarking using consistent segmentation and mesh steganalysis. The method is blind, statistical, and highly...     

A novel 3D mesh compression using mesh segmentation with multiple principal plane analysis

This paper proposes a novel scheme for 3D model compression based on mesh segmentation using multiple principal plane analysis. This algorithm first performs a mesh segmentation scheme, based on fusion of the well-known k-means clustering and the proposed principal plane analysis to separate the input 3D mesh into a set of disjointed polygonal regions. The boundary indexing scheme for the whole object is created by assembling local regions. Finally, the current work proposes a triangle traversal scheme to encode the connectivity and geometry information simultaneously for every patch under the guidance of the boundary indexing scheme. Simulation results demonstrate that the proposed algorithm obtains good performance in terms of compression rate and reconstruction quality.     

基于特殊内容的图像分割压缩方法

针对扫描的简历图像存储所面临的压缩比和恢复质量之间的矛盾,提出了一种将简历图像分割压缩的方法,分析了简历图像分割压缩存储的可行性和有效性,找出分割压缩的难点和关键,并结合应用给出了一套具体的解决方法,实现了一个应用此种方法的图像分割压缩转换系统。对系统进行了测试,测试结果表明,该方法是一套简单有效的方法,有效地兼顾了压缩比和图像恢复质量。     

Low-complexity progressive image transmission scheme based on quadtree segmentation

A novel progressive image transmission scheme based on the quadtree segmentation technique is introduced in this paper. A 3-level quadtree is used in the quadtree segmentation technique to partition the original image into blocks of different sizes. Image blocks of different sizes are encoded by their block mean values. The relatively addressing technique is employed to cut down the storage cost of block mean values.In the proposed scheme, the number of image hierarchies can be adaptively selected according to the specific applications. By exploiting inter-pixel correlation and differently sized blocks for segmentation, the proposed scheme provides good image qualities at low bit rates and consumes very little computational cost in both image encoding and decoding procedures. It is quite suitable for real-time progressive image transmission.     

Boundary-trimmed 3D triangular mesh segmentation based on iterative merging strategy

This paper presents a segmentation algorithm for 3D triangular mesh data. The proposed algorithm uses iterative merging of adjacent triangle pairs based on their orientations. The oversegmented regions are merged again in an iterative region merging process. Finally, the noisy boundaries of each region are refined. The boundaries of each region contain perceptually important geometric information of the entire mesh model. According to the purpose of the segmentation, the proposed mesh-segmentation algorithm supports various types of segmentation by controlling parameters.     

Rate-distortion optimization for progressive compression of 3D mesh with color attributes

We propose a new lossless progressive compression algorithm based on rate-distortion optimization for meshes with color attributes; the quantization precision of both the geometry and the color information is adapted to each intermediate mesh during the encoding/decoding process. This quantization precision can either be optimally determined with the use of a mesh distortion measure or quasi-optimally decided based on an analysis of the mesh complexity in order to reduce the calculation time. Furthermore, we propose a new metric which estimates the geometry and color importance of each vertex during the simplification in order to faithfully preserve the feature elements. Experimental results show that our method outperforms the state-of-the-art algorithm for colored meshes and competes with the most efficient algorithms for non-colored meshes.     

A new CAD mesh segmentation method, based on curvature tensor analysis

This paper presents a new and efficient algorithm for the decomposition of 3D arbitrary triangle meshes and particularly optimized triangulated CAD meshes. The algorithm is based on the curvature tensor field analysis and presents two distinct complementary steps: a region based segmentation, which is an improvement of that presented by Lavoue et al. [Lavoue G, Dupont F, Baskurt A. Constant curvature region decomposition of 3D-meshes by a mixed approach vertex-triangle, J WSCG 2004;12(2):245-52] and which decomposes the object into near constant curvature patches, and a boundary rectification based on curvature tensor directions, which corrects boundaries by suppressing their artefacts or discontinuities. Experiments conducted on various models including both CAD and natural objects, show satisfactory results. Resulting segmented patches, by virtue of their properties (homogeneous curvature, clean boundaries) are particularly adapted to computer graphics tasks like parametric or subdivision surface fitting in an adaptive compression objective.     

Transitive mesh space of a progressive mesh

The paper investigates the set of all selectively refined meshes that can be obtained from a progressive mesh. We call the set the transitive mesh space of a progressive mesh and present a theoretical analysis of the space. We define selective edge collapse and vertex split transformations, which we use to traverse all selectively refined meshes in the transitive mesh space. We propose a complete selective refinement scheme for a progressive mesh based on the transformations and compare the scheme with previous selective refinement schemes in both theoretical and experimental ways. In our comparison, we show that the complete scheme always generates selectively refined meshes with smaller numbers of vertices and faces than previous schemes for a given refinement criterion. The concept of dual pieces of the vertices in the vertex hierarchy plays a central role in the analysis of the transitive mesh space and the design of selective edge collapse and vertex split transformations.     

A framework for unsupervised mesh based segmentation of moving objects

Multimedia analysis usually deals with a large amount of video data with a significant number of moving objects. Often it is necessary to reduce the amount of data and to represent the video in terms of moving objects and events. Event analysis can be built on the detection of moving objects. In order to automatically process a variety of video content in different domain, largely unsupervised moving object segmentation algorithms are needed. We propose a fully unsupervised system for moving object segmentation that does not require any restriction on the video content. Our approach to extract moving objects relies on a mesh-based combination of results from colour segmentation (Mean Shift) and motion segmentation by feature point tracking (KLT tracker). The proposed algorithm has been evaluated using precision and recall measures for comparing moving objects and their colour segmented regions with manually labelled ground truth data. Results show that the algorithm is comparable to other state-of-the-art algorithms. The extracted information is used in a search and retrieval tool. For that purpose a moving object representation in MPEG-7 is implemented. It facilitates high performance indexing and retrieval of moving objects and events in large video databases, such as the search for similar moving objects occurring in a certain period.     

Optimised mesh traversal for dynamic mesh compression

During the last decade many algorithms for compressing 3D animations represented by sequences of triangular meshes have been proposed. Most of these algorithms are lossy in their nature, i.e. the reconstructed data do not exactly match the algorithm input.Quite surprisingly, most of the existing algorithms mainly use only general compression techniques, such as entropy coding, quantisation, PCA or wavelet decomposition, while the inherent geometrical properties of the compressed surface remain unexploited. In this paper we focus on geometry specific optimisation: we extend the PCA-based dynamic mesh compression by optimising the order in which the mesh is traversed. By considering the distribution of residuals and optimising the gate selection strategy we achieve data rate reductions by 5.9–29.1% over the existing approaches in the experiments, while the error introduced by compression remains unchanged. This optimisation improves the performance of our encoder above the performance of current state of the art algorithms.     

Protrusion-oriented 3D mesh segmentation

In this paper, we present a segmentation algorithm which partitions a mesh based on the premise that a 3D object consists of a core body and its constituent protrusible parts. Our approach is based on prominent feature extraction and core approximation and segments the mesh into perceptually meaningful components. Based upon the aforementioned premise, we present a methodology to compute the prominent features of the mesh, to approximate the core of the mesh and finally to trace the partitioning boundaries which will be further refined using a minimum cut algorithm. Although the proposed methodology is aligned with a general framework introduced by Lin et al. (IEEE Trans. Multimedia 9(1):46–57, 2007), new approaches have been introduced for the implementation of distinct stages of the framework leading to improved efficiency and robustness. The evaluation of the proposed algorithm is addressed in a consistent framework wherein a comparison with the state of the art is performed.     

Double watermarks of 3D mesh model based on feature segmentation and redundancy information

This paper presents a new robust, blind and good imperceptibility 3D mesh double watermarks algorithm. Two different kinds of watermarks are embedded into one 3D mesh model. One watermarking algorithm based on mesh feature segmentation and the DCT transformation, the other based on redundancy information of 3D model. The two watermarks do not disturb each other during embedding and extracting. Several mesh models are applied to test the robustness, imperceptibility and efficiency of the proposed algorithm. The experimental results show that the proposed watermark scheme can not only keep good imperceptibility but also resist various attacks, such as similarity transformations (translation, rotation, scaling and combinations of the three operations), file attack, signal processing attacks (noising, smoothing and vertex coordinate quantization) and connectivity attacks (cropping).     

Design of data segmentation and data compression operators based on projective morphological decompositions

Problems of morphological data segmentation and compression are addressed within the framework of projective morphology. Schemes for design of morphological segmentation operators with and without the loss of information based on equivalent transformations of bases of morphological decomposition are proposed. The projectivity of the obtained operations of segmentation with information loss is proved for two main classes of operators of morphological projection: minimum-distance (minimum deviation norm) and monotonic projectors. Information—entropy criteria of optimal finding of segmentation parameters are considered. It is shown that the choice of optimal segmentation parameters depends on the informativity (sample size) of the source data.     

Motion-based mesh segmentation using augmented silhouettes

Motion-based segmentation, the problem of detecting rigid parts of an articulated three-dimensional shape, is an open challenge that has several applications in mesh animation, compression, and interpolation. We present a novel approach that uses the visual perception of the shape and its motion to distinguish the rigid from the deformable parts of the object. Using two-dimensional projections of the different shape poses with respect to a number of different view points, we derive a set of one-dimensional curves, which form a superset of the mesh silhouettes. Analysing these augmented silhouettes, we identify the vertices of the mesh that correspond to the deformable parts, and a subsequent clustering approach, which is based on the diffusion distance, yields a motion-based segmentation of the shape.     

CAD mesh model segmentation by clustering

CAD mesh models have been widely employed in current CAD/CAM systems, where it is quite useful to recognize the features of the CAD mesh models. The first step of feature recognition is to segment the CAD mesh model into meaningful parts. Although there are lots of mesh segmentation methods in literature, the majority of them are not suitable to CAD mesh models. In this paper, we design a mesh segmentation method based on clustering, dedicated to the CAD mesh model. Specifically, by the agglomerative clustering method, the given CAD mesh model is first clustered into the sparse and dense triangle regions. Furthermore, the sparse triangle region is separated into planar regions, cylindrical regions, and conical regions by the Gauss map of the triangular faces and Hough transformation; the dense triangle region is also segmented by the mean shift operation performed on the mean curvature field defined on the mesh faces. Lots of empirical results demonstrate the effectiveness and efficiency of the CAD mesh segmentation method in this paper.     

Normal vector compression of 3D mesh model based on clustering and relative indexing

As the transmission of 3D shape models through Internet becomes more important, the compression issue of shape models gets more critical. While the compressions of topology and geometry have been explored significantly, the same issue for normal vectors has not yet been studied as much as it deserves.

Presented in this paper is an approach to compress the normal vectors of a 3D mesh model using the concept of clustering and relative indexing. The model is assumed to be manifold triangular mesh model with normal vectors associated with vertices. The proposed scheme clusters the normal vectors of given model and the representative normal vector of each cluster is referred to via a mixed use of relative as well as absolute indexing concepts. It turns out that the proposed approach achieves a significant compression ratio (less than 10% of the original VRML model files) without a serious sacrifice of the visual quality.     

Depth compression via planar segmentation

Augmented Reality applications are set to revolutionize the smartphone industry due to the integration of RGB-D sensors into mobile devices. Given the large number of smartphone users, efficient storage and transmission of RGB-D data is of paramount interest to the research community. While there exist Video Coding Standards such as HEVC and H.264/AVC for compression of RGB/texture component, the coding of depth data is still an area of active research. This paper presents a method for coding depth videos, captured from mobile RGB-D sensors, by planar segmentation. The segmentation algorithm is based on Markov Random Field assumptions on depth data and solved using Graph Cuts. While all prior works based on this approach remain restricted to images only and under noise-free conditions, this paper presents an efficient solution to planar segmentation in noisy depth videos. Also presented is a unique method to encode depth based on its segmented planar representation. Experiments on depth captured from a noisy sensor (Microsoft Kinect) shows superior Rate-Distortion performance over the 3D extension of HEVC codec.

     

Spectral 3D mesh segmentation with a novel single segmentation field

We present an automatic mesh segmentation framework that achieves 3D segmentation in two stages, hierarchical spectral analysis and isoline-based boundary detection. During the hierarchical spectral analysis stage, a novel segmentation field is defined to capture a concavity-aware decomposition of eigenvectors from a concavity-aware Laplacian. Specifically, a sufficient number of eigenvectors is first adaptively selected and simultaneously partitioned into sub-eigenvectors through spectral clustering. Next, on the sub-eigenvectors level, we evaluate the confidence of identifying a spectral-sensitive mesh boundary for each sub-eigenvector by two joint measures, namely, inner variations and part oscillations. The selection and combination of sub-eigenvectors are thereby formulated as an optimization problem to generate a single segmentation field. In the isoline-based boundary detection stage, the segmentation boundaries are recognized by a divide-merge algorithm and a cut score, which respectively filters and measures desirable isolines from the concise single segmentation field. Experimental results on the Princeton Segmentation Benchmark and a number of other complex meshes demonstrate the effectiveness of the proposed method, which is comparable to recent state-of-the-art algorithms.