arXiv:2610.34233v1 Announce Type: new
Abstract: High-resolution 3D Gaussian Splatting training increases pixel and Gaussian-tile intersection counts, expanding rasterization caches that coexist with...
By Xiaobin Deng, Changyu Diao, Min Li, Duanqing Xu
TileSkipper introduces a region‑adaptive tile pruning strategy for 3D Gaussian splatting rasterizers, assigning a static per‑Gaussian contribution cutoff policy that is calibrated on a frozen checkpoint. The method partitions Gaussians into 64 groups, uses a single‑byte policy per Gaussian, and requires no runtime updates or additional kernels. Across 13 benchmark scenes, TileSkipper achieves up to 1.238× speedups with negligible PSNR loss, and integrates with existing opacity‑aware bounds for further compiler‑only gains.
By Jingxing Li, Yongjae Lee, Deliang Fan, Abhay Kumar Yadav, Cheng Peng, Rama Chellappa
Efficient Confidence-Aware Densification for 3DGS proposes a new densification strategy for 3D Gaussian Splatting that replaces opacity-based sampling with error-driven placement of primitives. The method keeps a fixed primitive budget and places new primitives where image error and low learned confidence are highest, relocating under-contributing primitives accordingly. Across 24 scenes, it outperforms existing fixed-budget methods—including 3DGS-MCMC, ConeGS, and EDGS—in PSNR, SSIM, and LPIPS at budgets from 100k to 2M primitives, and can also leverage prior-based initialization to match or exceed prior methods.
By Manuel Hofer, Thomas K\"ohler, Felix Windisch, Bernhard Kerbl, Markus Steinberger, Lukas Radl
arXiv:2609.10307v1 Announce Type: cross
Abstract: 3D Gaussian Splatting (3DGS) renders novel views in real time, but an uncertainty heatmap does not certify that a rendered view meets a certain predi...
By Junzheng Chu, Bin Pan, Zhenwei Shi
Gauss What You Need: Compact Gaussian Splatting Across Scene Scales introduces TangoGS, a method that automatically selects the number of Gaussian primitives for 3D Gaussian Splatting by combining capture-derived model sizing with training-based adaptation. The approach first estimates a learning allowance based on the capture’s total pixels, then adjusts the number of Gaussians during training according to reconstruction quality. On standard benchmarks, TangoGS matches the best baseline’s PSNR while using 48% fewer Gaussians, and on larger captures it scales automatically to achieve the highest mean PSNR with 2.3× more Gaussians.
By Afif Boudaoud, Jiayi Liu, Alexandru Calotoiu, Torsten Hoefler
arXiv:2611.05593v1 Announce Type: cross
Abstract: Can a neural network learn to render 3D Gaussian splats on its own, with no rasterizer in the renderer? We present GaussianRenderFormer, a transforme...
By Shahaf Valencia Levy, Sagie Benaim