A Survey of Surface Reconstruction from Point Clouds
Matthew Berger,Andrea Tagliasacchi,Lee M. Seversky,Pierre Alliez,Gaël Guennebaud,Joshua A. Levine,Andrei Sharf,Cláudio T. Silva +7 more
TLDR
A holistic view of surface reconstruction is considered, which shows a detailed characterization of the field, highlights similarities between diverse reconstruction techniques and provides directions for future work in surface reconstruction.Abstract:
The area of surface reconstruction has seen substantial progress in the past two decades. The traditional problem addressed by surface reconstruction is to recover the digital representation of a physical shape that has been scanned, where the scanned data contain a wide variety of defects. While much of the earlier work has been focused on reconstructing a piece-wise smooth representation of the original shape, recent work has taken on more specialized priors to address significantly challenging data imperfections, where the reconstruction can take on different representations-not necessarily the explicit geometry. We survey the field of surface reconstruction, and provide a categorization with respect to priors, data imperfections and reconstruction output. By considering a holistic view of surface reconstruction, we show a detailed characterization of the field, highlight similarities between diverse reconstruction techniques and provide directions for future work in surface reconstruction.read more
Citations
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Journal ArticleDOI
Point convolutional neural networks by extension operators
TL;DR: Evaluation of PCNN on three central point cloudlearning benchmarks convincingly outperform competing point cloud learning methods, and the vast majority of methods working with more informative shape representations such as surfaces and/or normals.
Posted Content
Implicit Geometric Regularization for Learning Shapes
TL;DR: It is observed that a rather simple loss function, encouraging the neural network to vanish on the input point cloud and to have a unit norm gradient, possesses an implicit geometric regularization property that favors smooth and natural zero level set surfaces, avoiding bad zero-loss solutions.
Book ChapterDOI
EC-Net: An Edge-Aware Point Set Consolidation Network
TL;DR: This paper presents the first deep learning based edge-aware technique to facilitate the consolidation of point clouds, and trains the network to process points grouped in local patches, and train it to learn and help consolidate points, deliberately for edges.
Proceedings ArticleDOI
SAL: Sign Agnostic Learning of Shapes From Raw Data
Matan Atzmon,Yaron Lipman +1 more
TL;DR: Sign Agnostic Learning (SAL) as discussed by the authors is a deep learning approach for learning implicit shape representations directly from raw, unsigned geometric data, such as point clouds and triangle soups.
Journal ArticleDOI
ConvPoint: Continuous convolutions for point cloud processing
TL;DR: A generalization of discrete convolutional neural networks in order to deal with point clouds by replacing discrete kernels by continuous ones is proposed, which is simple, allows arbitrary point cloud sizes and can easily be used for designing neural networks similarly to 2D CNNs.
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