Unsupervised learning with generative models has the potential of discovering rich representations of 3D scenes. While geometric deep learning has explored 3D-structure-aware representations of scene geometry, these models typically require explicit 3D supervision. Emerging neural scene representations can be trained only with posed 2D images, but existing methods ignore the three-dimensional structure of scenes. We propose Scene Representation Networks (SRNs), a continuous, 3D-structure-aware scene representation that encodes both geometry and appearance. SRNs represent scenes as continuous functions that map world coordinates to a feature representation of local scene properties. By formulating the image formation as a differentiable ray-marching algorithm, SRNs can be trained end-to-end from only 2D images and their camera poses, without access to depth or shape. This formulation naturally generalizes across scenes, learning powerful geometry and appearance priors in the process. We demonstrate the potential of SRNs by evaluating them for novel view synthesis, few-shot reconstruction, joint shape and appearance interpolation, and unsupervised discovery of a non-rigid face model.
Vincent Sitzmann (Stanford University)
Vincent is a fourth year Ph.D. student in the Stanford Computational Imaging Laboratory, advised by Prof. Gordon Wetzstein. His research interest lies in 3D-structure-aware neural scene representations - a novel way for AI to represent information on our 3D world. The goal is to allow AI to perform intelligent 3D reasoning, such as inferring a complete model of a scene with information on geoemetry, material, lighting etc. from only few observations, a task that is simple for humans, but currently impossible for AI.
Michael Zollhoefer (Facebook Reality Labs)
Gordon Wetzstein (Stanford University)
Related Events (a corresponding poster, oral, or spotlight)
2019 Poster: Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations »
Wed Dec 11th 06:45 -- 08:45 PM Room East Exhibition Hall B + C