nerf2nerf: Pairwise Registration of Neural Radiance Fields

• URL: http://arxiv.org/abs/2211.01600v1
• Date: Thu, 3 Nov 2022 06:04:59 GMT
• Title: nerf2nerf: Pairwise Registration of Neural Radiance Fields
• Authors: Lily Goli, Daniel Rebain, Sara Sabour, Animesh Garg, Andrea Tagliasacchi
• Abstract summary: We introduce a technique for pairwise registration of neural fields that extends classical optimization-based local registration. We introduce the concept of a ''surface field'' -- a field distilled from a pre-trained NeRF model. We evaluate the effectiveness of our technique by introducing a dataset of pre-trained NeRF scenes.
• Score: 38.13011152344739
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a technique for pairwise registration of neural fields that extends classical optimization-based local registration (i.e. ICP) to operate on Neural Radiance Fields (NeRF) -- neural 3D scene representations trained from collections of calibrated images. NeRF does not decompose illumination and color, so to make registration invariant to illumination, we introduce the concept of a ''surface field'' -- a field distilled from a pre-trained NeRF model that measures the likelihood of a point being on the surface of an object. We then cast nerf2nerf registration as a robust optimization that iteratively seeks a rigid transformation that aligns the surface fields of the two scenes. We evaluate the effectiveness of our technique by introducing a dataset of pre-trained NeRF scenes -- our synthetic scenes enable quantitative evaluations and comparisons to classical registration techniques, while our real scenes demonstrate the validity of our technique in real-world scenarios. Additional results available at: https://nerf2nerf.github.io

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