Directed Distance Fields for Constant-Time Ray Queries on Gaussian Splatting

2026-05-30

AI Summary

This work addresses the limitation of 3D Gaussian Splatting, which supports only primary ray rendering and struggles to efficiently handle secondary ray queries required for shadows, ambient occlusion, and global illumination. To overcome this, the authors propose distilling a pre-trained 3D Gaussian Splatting scene into a lightweight directed distance field (DDF), enabling constant-time distance and hit queries for arbitrary rays. The method employs a mesh-free, end-to-end training pipeline with exact distance supervision to recover fine geometric details. The resulting DDF achieves 26–72× faster query speeds than sphere tracing, with memory and computational costs independent of scene complexity. Evaluated on 142 objects and real-world scenes, the approach produces high-quality secondary ray effects, achieving signal-to-noise ratios of 30.3 dB for shadows and 21.3 dB for ambient occlusion.

Abstract

3D Gaussian Splatting (3DGS) renders new views of a scene in real time. Like every rasterizer, it answers only primary rays, the rays from the camera through the image. It cannot trace the secondary rays that shadows, ambient occlusion, and global illumination need. We turn a trained 3DGS scene into a ray oracle by distilling a Directed Distance Function (DDF). The DDF is a small neural field. It takes a ray, given by an origin and a direction, and returns the distance to the first surface and whether the ray hits anything. Each query is one forward pass. The field is 52~MB, and its size does not depend on the number of Gaussians, so its cost and memory stay flat as the scene grows. We make three points. First, we study what supervision a DDF needs. Depth rendered from the Gaussians is too blurry to teach thin parts, while clean distance supervision recovers them. Second, we measure speed. The DDF is 26 to 72 times faster than sphere tracing an equivalent signed distance field, and unlike a bounding volume hierarchy built over the Gaussians, even on dedicated RT-core hardware, its query time and memory do not grow with the scene. Third, we show a pipeline that needs no mesh: images give a 3DGS scene, a neural surface gives clean distances, and the DDF learns from them. We use the DDF as a secondary-ray oracle for global illumination. It reproduces reference ray-traced shadows at 30.3~dB and ambient occlusion at 21.3~dB across 142 objects, and on real captured scenes. Our codes are available at https://github.com/smlab-niser/ddf-gs.

Problem

• 3D Gaussian Splatting
• ray tracing
• secondary rays
• global illumination
• distance fields

Innovation

• Directed Distance Field
• 3D Gaussian Splatting
• Constant-Time Ray Query
• Neural Implicit Representation
• Global Illumination