REdiSplats: Ray Tracing for Editable Gaussian Splatting

Krzysztof Byrski, Grzegorz Wilczyński, Weronika Smolak-Dyżewska, Piotr Borycki, Dawid Baran +2 more
3/15/2025
cs.CV

No confident code implementation found for this paper.

Abstract

Gaussian Splatting (GS) has become one of the most important neural rendering algorithms. GS represents 3D scenes using Gaussian components with trainable color and opacity. This representation achieves high-quality renderings with fast inference. Regrettably, it is challenging to integrate such a solution with varying light conditions, including shadows and light reflections, manual adjustments, and a physical engine. Recently, a few approaches have appeared that incorporate ray-tracing or mesh primitives into GS to address some of these caveats. However, no such solution can simultaneously solve all the existing limitations of the classical GS. Consequently, we introduce REdiSplats, which employs ray tracing and a mesh-based representation of flat 3D Gaussians. In practice, we model the scene using flat Gaussian distributions parameterized by the mesh. We can leverage fast ray tracing and control Gaussian modification by adjusting the mesh vertices. Moreover, REdiSplats allows modeling of light conditions, manual adjustments, and physical simulation. Furthermore, we can render our models using 3D tools such as Blender or Nvdiffrast, which opens the possibility of integrating them with all existing 3D graphics techniques dedicated to mesh representations.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{byrski2025redisplats,
  title  = {REdiSplats: Ray Tracing for Editable Gaussian Splatting},
  author = {Krzysztof Byrski and Grzegorz Wilczyński and Weronika Smolak-Dyżewska and Piotr Borycki and Dawid Baran and Sławomir Tadeja and Przemysław Spurek},
  year   = {2025},
  eprint = {2503.12284},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2503.12284v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2503.12284.svg)](https://codeofpaper.com/paper/2503.12284)

Discussion