DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
This addresses the need for efficient and accurate 3D shape representation in computer graphics, vision, and robotics, offering a novel method that improves over existing approaches.
The paper tackled the problem of representing 3D shapes by introducing DeepSDF, a learned continuous Signed Distance Function that enables high-quality shape representation, interpolation, and completion from partial data, achieving state-of-the-art performance while reducing model size by an order of magnitude.
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.