Pith. sign in

REVIEW 2 cited by

3D Gaussian Ray Tracing: Fast Tracing of Particle Scenes

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2407.07090 v3 pith:W5C5EEID submitted 2024-07-09 cs.GR cs.CV

classification cs.GRcs.CV
keywords tracingparticlesgaussianboundingcomputerfastgraphicsintersections
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Particle-based representations of radiance fields such as 3D Gaussian Splatting have found great success for reconstructing and re-rendering of complex scenes. Most existing methods render particles via rasterization, projecting them to screen space tiles for processing in a sorted order. This work instead considers ray tracing the particles, building a bounding volume hierarchy and casting a ray for each pixel using high-performance GPU ray tracing hardware. To efficiently handle large numbers of semi-transparent particles, we describe a specialized rendering algorithm which encapsulates particles with bounding meshes to leverage fast ray-triangle intersections, and shades batches of intersections in depth-order. The benefits of ray tracing are well-known in computer graphics: processing incoherent rays for secondary lighting effects such as shadows and reflections, rendering from highly-distorted cameras common in robotics, stochastically sampling rays, and more. With our renderer, this flexibility comes at little cost compared to rasterization. Experiments demonstrate the speed and accuracy of our approach, as well as several applications in computer graphics and vision. We further propose related improvements to the basic Gaussian representation, including a simple use of generalized kernel functions which significantly reduces particle hit counts.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Volumetrically Consistent 3D Gaussian Rasterization

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A drop-in alpha computation for 3DGS rasterizers, derived from analytic volumetric transmittance, improves edge and perceptual metrics and transfers to tomography.

  2. 6DOPE-GS: Online 6D Object Pose Estimation using Gaussian Splatting

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A model-free system that jointly optimizes 2D Gaussian Splatting and keyframe poses to track and reconstruct unknown objects live from RGB-D video.

Pith tools