Depth-supervised NeRF: Fewer Views and Faster Training for Free

• URL: http://arxiv.org/abs/2107.02791v1
• Date: Tue, 6 Jul 2021 17:58:35 GMT
• Title: Depth-supervised NeRF: Fewer Views and Faster Training for Free
• Authors: Kangle Deng, Andrew Liu, Jun-Yan Zhu, and Deva Ramanan
• Abstract summary: DS-NeRF is a loss for learning neural radiance fields that takes advantage of readily-available depth supervision. We find that DS-NeRF can render more accurate images given fewer training views while training 2-6x faster.
• Score: 66.16386801362643
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One common failure mode of Neural Radiance Field (NeRF) models is fitting incorrect geometries when given an insufficient number of input views. We propose DS-NeRF (Depth-supervised Neural Radiance Fields), a loss for learning neural radiance fields that takes advantage of readily-available depth supervision. Our key insight is that sparse depth supervision can be used to regularize the learned geometry, a crucial component for effectively rendering novel views using NeRF. We exploit the fact that current NeRF pipelines require images with known camera poses that are typically estimated by running structure-from-motion (SFM). Crucially, SFM also produces sparse 3D points that can be used as ``free" depth supervision during training: we simply add a loss to ensure that depth rendered along rays that intersect these 3D points is close to the observed depth. We find that DS-NeRF can render more accurate images given fewer training views while training 2-6x faster. With only two training views on real-world images, DS-NeRF significantly outperforms NeRF as well as other sparse-view variants. We show that our loss is compatible with these NeRF models, demonstrating that depth is a cheap and easily digestible supervisory signal. Finally, we show that DS-NeRF supports other types of depth supervision such as scanned depth sensors and RGBD reconstruction outputs.

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