575 research outputs found
Co-skeletons:Consistent curve skeletons for shape families
We present co-skeletons, a new method that computes consistent curve skeletons for 3D shapes from a given family. We compute co-skeletons in terms of sampling density and semantic relevance, while preserving the desired characteristics of traditional, per-shape curve skeletonization approaches. We take the curve skeletons extracted by traditional approaches for all shapes from a family as input, and compute semantic correlation information of individual skeleton branches to guide an edge-pruning process via skeleton-based descriptors, clustering, and a voting algorithm. Our approach achieves more concise and family-consistent skeletons when compared to traditional per-shape methods. We show the utility of our method by using co-skeletons for shape segmentation and shape blending on real-world data
Analysis of face and segment level descriptors for robust 3D co-segmentation
Analysis of face and segment level descriptors for robust 3D co-segmentation
Learning on 3D meshes with laplacian encoding and pooling
3D models are commonly used in computer vision and graphics. With the wider availability of mesh data, an efficient and intrinsic deep learning approach to processing 3D meshes is in great need. Unlike images, 3D meshes have irregular connectivity, requiring careful design to capture relations in the data. To utilize the topology information while staying robust under different triangulations, we propose to encode mesh connectivity using Laplacian spectral analysis, along with mesh feature aggregation blocks (MFABs) that can split the surface domain into local pooling patches and aggregate global information amongst them. We build a mesh hierarchy from fine to coarse using Laplacian spectral clustering, which is flexible under isometric transformations. Inside the MFABs there are pooling layers to collect local information and multi-layer perceptrons to compute vertex features of increasing complexity. To obtain the relationships among different clusters, we introduce a Correlation Net to compute a correlation matrix, which can aggregate the features globally by matrix multiplication with cluster features. Our network architecture is flexible enough to be used on meshes with different numbers of vertices. We conduct several experiments including shape segmentation and classification, and our method outperforms state-of-the-art algorithms for these tasks on the ShapeNet and COSEG datasets
Deep Learning based 3D Segmentation: A Survey
3D object segmentation is a fundamental and challenging problem in computer
vision with applications in autonomous driving, robotics, augmented reality and
medical image analysis. It has received significant attention from the computer
vision, graphics and machine learning communities. Traditionally, 3D
segmentation was performed with hand-crafted features and engineered methods
which failed to achieve acceptable accuracy and could not generalize to
large-scale data. Driven by their great success in 2D computer vision, deep
learning techniques have recently become the tool of choice for 3D segmentation
tasks as well. This has led to an influx of a large number of methods in the
literature that have been evaluated on different benchmark datasets. This paper
provides a comprehensive survey of recent progress in deep learning based 3D
segmentation covering over 150 papers. It summarizes the most commonly used
pipelines, discusses their highlights and shortcomings, and analyzes the
competitive results of these segmentation methods. Based on the analysis, it
also provides promising research directions for the future.Comment: Under review of ACM Computing Surveys, 36 pages, 10 tables, 9 figure
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