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cs.CV2026

Semantic Foam: Unifying Spatial and Semantic Scene Decomposition

Amr Sharafeldin, Shrisudhan Govindarajan, Thomas Walker +4

Modern scene reconstruction methods, such as 3D Gaussian Splatting, deliver photo-realistic novel view synthesis at real-time speeds, yet their adoption in interactive graphics app…

cs.CV2025

Weakly Supervised Point Cloud Segmentation via Conservative Propagation of Scene-level Labels

Shaobo Xia, Jun Yue, Kacper Kania +4

We propose a weakly supervised semantic segmentation method for point clouds that predicts "per-point" labels from just "whole-scene" annotations. The key challenge here is the dis…

cs.CV2025

StochasticSplats: Stochastic Rasterization for Sorting-Free 3D Gaussian Splatting

Shakiba Kheradmand, Delio Vicini, George Kopanas +4

3D Gaussian splatting (3DGS) is a popular radiance field method, with many application-specific extensions. Most variants rely on the same core algorithm: depth-sorting of Gaussian…

cs.CV2025

NoKSR: Kernel-Free Neural Surface Reconstruction via Point Cloud Serialization

Zhen Li, Weiwei Sun, Shrisudhan Govindarajan +4

We present a novel approach to large-scale point cloud surface reconstruction by developing an efficient framework that converts an irregular point cloud into a signed distance fie…

cs.CV2025

3D Gaussian Splatting as Markov Chain Monte Carlo

Shakiba Kheradmand, Daniel Rebain, Gopal Sharma +6

While 3D Gaussian Splatting has recently become popular for neural rendering, current methods rely on carefully engineered cloning and splitting strategies for placing Gaussians, w…

cs.CV2025

Radiant Foam: Real-Time Differentiable Ray Tracing

Shrisudhan Govindarajan, Daniel Rebain, Kwang Moo Yi +1

Research on differentiable scene representations is consistently moving towards more efficient, real-time models. Recently, this has led to the popularization of splatting methods,…