Log NeRF: Comparing Spaces for Learning Radiance Fields


Sihe Chen (Northeastern University), Luv Verma (Northeastern University), Bruce A. Maxwell (Northeastern University)
The 35th British Machine Vision Conference
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Abstract

Neural Radiance Fields (NeRF) have achieved remarkable results in novel view synthesis, typically using sRGB images for supervision. However, little attention has been paid to the color space in which the network is learning the radiance field representation. Inspired by the Bi-Illuminant Dichromatic Reflection (BIDR) model, which suggests that a logarithmic transformation simplifies the separation of illumination and reflectance, we hypothesize that log-RGB space enables NeRF to learn a more compact and effective representation of scene appearance. To test this, we captured approximately 30 videos using a GoPro camera, ensuring linear data recovery through inverse encoding. We trained NeRF models under various color space interpretations—linear, sRGB, GPLog, and log RGB—by converting each network output to a common color space before rendering and loss computation, enforcing representation learning in different color spaces. Quantitative and qualitative evaluations demonstrate that using a log RGB color space consistently improves rendering quality, exhibits greater robustness across scenes, and performs particularly well in low-light conditions while using the same bit-depth input images. Further analysis across different network sizes and NeRF variants confirms the generalization and stability of the log-space advantage.

Citation

@inproceedings{Chen_2025_BMVC,
author    = {Sihe Chen and Luv Verma and Bruce A. Maxwell},
title     = {Log NeRF: Comparing Spaces for Learning Radiance Fields},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_402/paper.pdf}
}


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