DeLiRa: Self-Supervised Depth, Light, and Radiance Fields

• URL: http://arxiv.org/abs/2304.02797v1
• Date: Thu, 6 Apr 2023 00:16:25 GMT
• Title: DeLiRa: Self-Supervised Depth, Light, and Radiance Fields
• Authors: Vitor Guizilini, Igor Vasiljevic, Jiading Fang, Rares Ambrus, Sergey Zakharov, Vincent Sitzmann, Adrien Gaidon
• Abstract summary: Differentiable volumetric rendering is a powerful paradigm for 3D reconstruction and novel view synthesis. Standard volume rendering approaches struggle with degenerate geometries in the case of limited viewpoint diversity. In this work, we propose to use the multi-view photometric objective as a geometric regularizer for volumetric rendering.
• Score: 32.350984950639656
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Differentiable volumetric rendering is a powerful paradigm for 3D reconstruction and novel view synthesis. However, standard volume rendering approaches struggle with degenerate geometries in the case of limited viewpoint diversity, a common scenario in robotics applications. In this work, we propose to use the multi-view photometric objective from the self-supervised depth estimation literature as a geometric regularizer for volumetric rendering, significantly improving novel view synthesis without requiring additional information. Building upon this insight, we explore the explicit modeling of scene geometry using a generalist Transformer, jointly learning a radiance field as well as depth and light fields with a set of shared latent codes. We demonstrate that sharing geometric information across tasks is mutually beneficial, leading to improvements over single-task learning without an increase in network complexity. Our DeLiRa architecture achieves state-of-the-art results on the ScanNet benchmark, enabling high quality volumetric rendering as well as real-time novel view and depth synthesis in the limited viewpoint diversity setting.

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