NeRF++: Analyzing and Improving Neural Radiance Fields

• URL: http://arxiv.org/abs/2010.07492v2
• Date: Wed, 21 Oct 2020 18:53:21 GMT
• Title: NeRF++: Analyzing and Improving Neural Radiance Fields
• Authors: Kai Zhang, Gernot Riegler, Noah Snavely, Vladlen Koltun
• Abstract summary: Neural Radiance Fields (NeRF) achieve impressive view synthesis results for a variety of capture settings. NeRF fits multi-layer perceptrons representing view-invariant opacity and view-dependent color volumes to a set of training images. We address a parametrization issue involved in applying NeRF to 360 captures of objects within large-scale, 3D scenes.
• Score: 117.73411181186088
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural Radiance Fields (NeRF) achieve impressive view synthesis results for a variety of capture settings, including 360 capture of bounded scenes and forward-facing capture of bounded and unbounded scenes. NeRF fits multi-layer perceptrons (MLPs) representing view-invariant opacity and view-dependent color volumes to a set of training images, and samples novel views based on volume rendering techniques. In this technical report, we first remark on radiance fields and their potential ambiguities, namely the shape-radiance ambiguity, and analyze NeRF's success in avoiding such ambiguities. Second, we address a parametrization issue involved in applying NeRF to 360 captures of objects within large-scale, unbounded 3D scenes. Our method improves view synthesis fidelity in this challenging scenario. Code is available at https://github.com/Kai-46/nerfplusplus.

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