arXiv Machine Learning
Oct 9

GaussianBench: Physics-Fidelity Evaluation for Gaussian Scene Representations

GaussianBench is a physics‑fidelity evaluation suite designed for physics‑integrated Gaussian scene representations. It tests key physical properties—such as conservation, continuum response, material coupling, covariance transport, rendering, thermal phase change, and counterfactual response—using frozen scenes, simulator‑independent scorers, and analytical or measured references. The benchmark distinguishes physical failures from unsupported capabilities and invalid comparisons, and the authors also present GaussianFlesh, a thermomechanical reference system that couples rendering primitives with continuum material points via an MPM solver.

By Chukwudalu Dumebi-Kachikwu
arXiv Computer Vision
Sep 4

F4Splat: Feed-Forward Predictive Densification for Feed-Forward 3D Gaussian Splatting

F4Splat introduces a feed‑forward predictive densification strategy for 3D Gaussian splatting that allocates Gaussians based on a densification‑score guided by spatial complexity and multi‑view overlap. The method predicts per‑region scores to estimate required Gaussian density, enabling explicit control over the total Gaussian budget without retraining. This adaptive allocation reduces redundancy in simple regions and minimizes duplicate Gaussians across overlapping views, yielding compact yet high‑quality 3D representations and superior novel‑view synthesis performance with fewer Gaussians.

By Injae Kim, Chaehyeon Kim, Minseong Bae, Minseok Joo, Hyunwoo J. Kim