Radiance Surfaces: Optimizing Surface Representations with a 5D Radiance Field Loss

TL;DR

This paper introduces a simple modification to radiance volume reconstruction that enables direct supervision of radiance surfaces, removing the need for ray marching and alpha blending, and resulting in explicit, high-quality surface models.

Contribution

The authors propose a novel loss function modification that directly supervises the radiance surface, simplifying the scene representation process and improving surface interpretability.

Findings

Achieves comparable PSNR to baseline with faster convergence.

Removes the need for alpha blending and ray marching.

Produces explicit, high-quality radiance surfaces.

Abstract

We present a fast and simple technique to convert images into a radiance surface-based scene representation. Building on existing radiance volume reconstruction algorithms, we introduce a subtle yet impactful modification of the loss function requiring changes to only a few lines of code: instead of integrating the radiance field along rays and supervising the resulting images, we project the training images into the scene to directly supervise the spatio-directional radiance field. The primary outcome of this change is the complete removal of alpha blending and ray marching from the image formation model, instead moving these steps into the loss computation. In addition to promoting convergence to surfaces, this formulation assigns explicit semantic meaning to 2D subsets of the radiance field, turning them into well-defined radiance surfaces. We finally extract a level set from this representation, which results in a high-quality radiance surface model. Our method retains much of the speed and quality of the baseline algorithm. For instance, a suitably modified variant of Instant NGP maintains comparable computational efficiency, while achieving an average PSNR that is only 0.1 dB lower. Most importantly, our method generates explicit surfaces in place of an exponential volume, doing so with a level of simplicity not seen in prior work.