DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation. Computer graphics, 3D computer vision and robotics communities have produced multiple approaches to representing 3D geometry for rendering and reconstruction. These provide trade-offs across fidelity, efficiency and compression capabilities. In this work, we introduce DeepSDF, a learned continuous Signed Distance Function (SDF) representation of a class of shapes that enables high quality shape representation, interpolation and completion from partial and noisy 3D input data. DeepSDF, like its classical counterpart, represents a shape’s surface by a continuous volumetric field: the magnitude of a point in the field represents the distance to the surface boundary and the sign indicates whether the region is inside (-) or outside (+) of the shape, hence our representation implicitly encodes a shape’s boundary as the zero-level-set of the learned function while explicitly representing the classification of space as being part of the shapes interior or not. While classical SDF’s both in analytical or discretized voxel form typically represent the surface of a single shape, DeepSDF can represent an entire class of shapes. Furthermore, we show state-of-the-art performance for learned 3D shape representation and completion while reducing the model size by an order of magnitude compared with previous work.
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References in zbMATH (referenced in 7 articles )
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- Sukumar, N.; Srivastava, Ankit: Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks (2022)
- Ping, Yuhan; Wei, Guodong; Yang, Lei; Cui, Zhiming; Wang, Wenping: Self-attention implicit function networks for 3D dental data completion (2021)
- Sun, Caixia; Zou, Lian; Fan, Cien; Shi, Yu; Liu, Yifeng: Enhancing adversarial attack transferability with multi-scale feature attack (2021)
- Deng, Hao; To, Albert C.: Topology optimization based on deep representation learning (DRL) for compliance and stress-constrained design (2020)
- Meta Platforms, Inc; Nikhila Ravi, Jeremy Reizenstein, David Novotny, Taylor Gordon, Wan-Yen Lo, Justin Johnson, Georgia Gkioxari: Accelerating 3D Deep Learning with PyTorch3D (2020) arXiv
- Nicolas Wagner, Ulrich Schwanecke: NeuralQAAD: An Efficient Differentiable Framework for High Resolution Point Cloud Compression (2020) arXiv
- Krishna Murthy Jatavallabhula, Edward Smith, Jean-Francois Lafleche, Clement Fuji Tsang, Artem Rozantsev, Wenzheng Chen, Tommy Xiang, Rev Lebaredian, Sanja Fidler: Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research (2019) arXiv