DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
Jeong Joon Park,Peter R. Florence,Julian Straub,Richard Newcombe,Steven Lovegrove +4 more
- pp 165-174
TLDR
DeepSDF as mentioned in this paper 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.Abstract:
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.read more
Citations
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NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
Ben Mildenhall,Pratul P. Srinivasan,Matthew Tancik,Jonathan T. Barron,Ravi Ramamoorthi,Ren Ng +5 more
TL;DR: This work describes how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrates results that outperform prior work on neural rendering and view synthesis.
Proceedings ArticleDOI
Occupancy Networks: Learning 3D Reconstruction in Function Space
TL;DR: In this paper, the authors propose Occupancy Networks, which implicitly represent the 3D surface as the continuous decision boundary of a deep neural network classifier, which can be used for learning-based 3D reconstruction methods.
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NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
Ben Mildenhall,Pratul P. Srinivasan,Matthew Tancik,Jonathan T. Barron,Ravi Ramamoorthi,Ren Ng +5 more
TL;DR: In this article, a fully-connected (non-convolutional) deep network is used to synthesize novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views.
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Instant neural graphics primitives with a multiresolution hash encoding
TL;DR: A versatile new input encoding that permits the use of a smaller network without sacrificing quality, thus significantly reducing the number of floating point and memory access operations is introduced, enabling training of high-quality neural graphics primitives in a matter of seconds, and rendering in tens of milliseconds at a resolution of 1920×1080.
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Differentiable Volumetric Rendering: Learning Implicit 3D Representations Without 3D Supervision
TL;DR: This work proposes a differentiable rendering formulation for implicit shape and texture representations, showing that depth gradients can be derived analytically using the concept of implicit differentiation, and finds that this method can be used for multi-view 3D reconstruction, directly resulting in watertight meshes.
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