Multi-Space Neural Radiance Fields

• URL: http://arxiv.org/abs/2305.04268v1
• Date: Sun, 7 May 2023 13:11:07 GMT
• Title: Multi-Space Neural Radiance Fields
• Authors: Ze-Xin Yin and Jiaxiong Qiu and Ming-Ming Cheng and Bo Ren
• Abstract summary: Existing Neural Radiance Fields (NeRF) methods suffer from the existence of reflective objects. We propose a multi-space neural radiance field (MS-NeRF) that represents the scene using a group of feature fields in parallel sub-spaces. Our approach significantly outperforms the existing single-space NeRF methods for rendering high-quality scenes.
• Score: 74.46513422075438
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Existing Neural Radiance Fields (NeRF) methods suffer from the existence of reflective objects, often resulting in blurry or distorted rendering. Instead of calculating a single radiance field, we propose a multi-space neural radiance field (MS-NeRF) that represents the scene using a group of feature fields in parallel sub-spaces, which leads to a better understanding of the neural network toward the existence of reflective and refractive objects. Our multi-space scheme works as an enhancement to existing NeRF methods, with only small computational overheads needed for training and inferring the extra-space outputs. We demonstrate the superiority and compatibility of our approach using three representative NeRF-based models, i.e., NeRF, Mip-NeRF, and Mip-NeRF 360. Comparisons are performed on a novelly constructed dataset consisting of 25 synthetic scenes and 7 real captured scenes with complex reflection and refraction, all having 360-degree viewpoints. Extensive experiments show that our approach significantly outperforms the existing single-space NeRF methods for rendering high-quality scenes concerned with complex light paths through mirror-like objects. Our code and dataset will be publicly available at https://zx-yin.github.io/msnerf.

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