Alpha Invariance: On Inverse Scaling Between Distance and Volume Density in Neural Radiance Fields

Joshua Ahn, Haochen Wang, Raymond A. Yeh, Greg Shakhnarovich; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 20396-20405

Abstract

Scale-ambiguity in 3D scene dimensions leads to magnitude-ambiguity of volumetric densities in neural radiance fields i.e. the densities double when scene size is halved and vice versa. We call this property alpha invariance. For NeRFs to better maintain alpha invariance we recommend 1) parameterizing both distance and volume densities in log space and 2) a discretization-agnostic initialization strategy to guarantee high ray transmittance. We revisit a few popular radiance field models and find that these systems use various heuristics to deal with issues arising from scene scaling. We test their behaviors and show our recipe to be more robust.

Related Material

[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Ahn_2024_CVPR, author = {Ahn, Joshua and Wang, Haochen and Yeh, Raymond A. and Shakhnarovich, Greg}, title = {Alpha Invariance: On Inverse Scaling Between Distance and Volume Density in Neural Radiance Fields}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {20396-20405} }