LOLNeRF: Learn from One Look

• URL: http://arxiv.org/abs/2111.09996v1
• Date: Fri, 19 Nov 2021 01:20:01 GMT
• Title: LOLNeRF: Learn from One Look
• Authors: Daniel Rebain, Mark Matthews, Kwang Moo Yi, Dmitry Lagun, Andrea Tagliasacchi
• Abstract summary: We present a method for learning a generative 3D model based on neural radiance fields. We show that, unlike existing methods, one does not need multi-view data to achieve this goal.
• Score: 22.771493686755544
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a method for learning a generative 3D model based on neural radiance fields, trained solely from data with only single views of each object. While generating realistic images is no longer a difficult task, producing the corresponding 3D structure such that they can be rendered from different views is non-trivial. We show that, unlike existing methods, one does not need multi-view data to achieve this goal. Specifically, we show that by reconstructing many images aligned to an approximate canonical pose with a single network conditioned on a shared latent space, you can learn a space of radiance fields that models shape and appearance for a class of objects. We demonstrate this by training models to reconstruct object categories using datasets that contain only one view of each subject without depth or geometry information. Our experiments show that we achieve state-of-the-art results in novel view synthesis and competitive results for monocular depth prediction.

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