ReSplat introduces a recurrent Gaussian splatting model that iteratively refines 3D Gaussians using the rendering error as a feedback signal, avoiding explicit gradient computation. The method starts from a compact reconstruction in a subsampled space, producing far fewer Gaussians than prior per‑pixel models, which reduces computational cost. Experiments on multiple datasets, view counts, and resolutions show state‑of‑the‑art performance with faster rendering speeds.
By Haofei Xu, Daniel Barath, Andreas Geiger, Marc Pollefeys
arXiv:2608.23549v1 Announce Type: new
Abstract: Rendering views using 3D scene representations such as Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or even point clouds produces...
By Khiem Vuong, Deva Ramanan, Srinivasa Narasimhan
The paper presents a training‑free filtering method for feed‑forward 3D Gaussian Splatting that removes transient distractors from 3D reconstructions. By excluding each input’s per‑view Gaussians and re‑rendering the scene, the method identifies inconsistent content through feature similarity and reconstruction error reduction. The approach improves novel‑view quality across multiple models and benchmarks while preserving clean scenes.
By Kangmin Seo, Jae-Pil Heo
Feed-forward 3D Gaussian Splatting enables efficient novel-view synthesis without per-scene optimization, but most existing methods assume a fixed set of context views and process them jointly. This limits their applicability to online scenarios where calibrated views arrive sequentially and the scene must be updated causally.
arXiv:2607. 17842v1 Announce Type: cross Abstract: Recent breakthroughs in 3D Gaussian Splatting (3DGS) have advanced neural rendering with high fidelity and speed.
By Tingjia Zhang, Bo Chen, Shengzhong Liu, Fan Wu, Guihai Chen
Rendering views using 3D scene representations such as Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or even point clouds produces artifacts when input views are sparse or target v...