Pith. sign in

REVIEW 3 cited by

Gaussian Frosting: Editable Complex Radiance Fields with Real-Time Rendering

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 2403.14554 v1 pith:YDQ5IJYB submitted 2024-03-21 cs.CV cs.GR

classification cs.CVcs.GR
keywords frostinggaussiangaussiansmesheditinglayerrenderingbase
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We propose Gaussian Frosting, a novel mesh-based representation for high-quality rendering and editing of complex 3D effects in real-time. Our approach builds on the recent 3D Gaussian Splatting framework, which optimizes a set of 3D Gaussians to approximate a radiance field from images. We propose first extracting a base mesh from Gaussians during optimization, then building and refining an adaptive layer of Gaussians with a variable thickness around the mesh to better capture the fine details and volumetric effects near the surface, such as hair or grass. We call this layer Gaussian Frosting, as it resembles a coating of frosting on a cake. The fuzzier the material, the thicker the frosting. We also introduce a parameterization of the Gaussians to enforce them to stay inside the frosting layer and automatically adjust their parameters when deforming, rescaling, editing or animating the mesh. Our representation allows for efficient rendering using Gaussian splatting, as well as editing and animation by modifying the base mesh. We demonstrate the effectiveness of our method on various synthetic and real scenes, and show that it outperforms existing surface-based approaches. We will release our code and a web-based viewer as additional contributions. Our project page is the following: https://anttwo.github.io/frosting/

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

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

  1. A Hybrid Neural-Microfacet BRDF Model for Real-Time Rendering

    cs.GR 2026-08 conditional novelty 6.0 of 10

    A hybrid BRDF model, combining a GGX analytical term with a tiny learned residual and gating network, fits measured materials more accurately than fully neural models at equal memory cost.

  2. EmbodiedOcc: Embodied 3D Occupancy Prediction for Vision-based Online Scene Understanding

    cs.CV 2024-12 conditional novelty 6.0 of 10

    EmbodiedOcc maintains an explicit global Gaussian memory that is progressively updated from monocular RGB frames, and it introduces a reorganized ScanNet benchmark for embodied 3D occupancy prediction.

  3. Neural Surface Priors for Editable Gaussian Splatting

    cs.CV 2024-11 conditional novelty 6.0 of 10

    A neural-surface-guided Gaussian splatting pipeline propagates mesh edits through a triangle soup proxy, enabling mesh-based editing of reconstructed scenes.

Pith tools