3D Gaussian Splatting (3DGS) enables high-fidelity and real-time 3D scene reconstruction, but scaling training to large-scale scenes requires optimizing hundreds of millions of Gaussians across multiple GPUs. Existing distributed approaches either partition scenes into isolated regions, causing global inconsistency, or rely on global Gaussian-level exchanges, which lead to substantial growth in inter-GPU communication and quickly dominate iteration time.
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:2506. 07069v2 Announce Type: replace-cross Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis, combining high-quality reconstruction with efficient rendering.
By Zhican Wang, Guanghui He, Lingjun Gao, Dantong Liu, Shell Xu Hu, Chen Zhang, Zhuoran Song, Nicholas Lane, Hongxiang Fan
InceptionGS is a method that improves large‑scale Gaussian splatting for scenes captured with unstructured view sampling. It starts from an initial Gaussian splatting and then selectively repairs regions that suffer from sparse views by incorporating adaptive generative priors, while preserving quality in well‑sampled areas. Experiments on real‑world scenes show that this balanced reconstruction‑generation approach yields higher‑fidelity results and works broadly across unstructured imagery.
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
InceptionGS is a method that improves large‑scale Gaussian splatting for scenes captured with unstructured view sampling. It starts from an initial Gaussian splatting and selectively repairs areas affected by sparse views by integrating scene‑ and view‑adaptive generative priors, while keeping well‑covered regions unchanged. Experiments on real‑world scenes show that this hybrid reconstruction‑generation approach yields higher‑fidelity results than existing methods.
By Tianheng Lu, Guangyu Wang, Ruqi Huang, Lu Fang
arXiv:2512.07197v2 Announce Type: replace
Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful explicit representation enabling real-time, high-fidelity 3D reconstruction and novel view s...
By Seokhyun Youn, Soohyun Lee, Geonho Kim, Weeyoung Kwon, Sung-Ho Bae, Jihyong Oh
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
The paper introduces SVRecon, a generalizable neural surface reconstruction framework that uses sparse volumetric representations to achieve high-resolution 3D reconstruction. It employs a two-stage architecture: first predicting occupied voxels with an occupancy network, then rendering only within those regions using specialized sparse algorithms. This approach allows reconstruction at resolutions up to 512³ on 32 GB hardware, producing smoother and more precise surfaces, especially in sparse-view scenarios.
By Aoxiang Fan, Corentin Dumery, Nicolas Talabot, Ming Xu, Hieu Le, Pascal Fua
arXiv:2608.22344v1 Announce Type: new
Abstract: 3D Gaussian Splatting (3DGS) achieves state-of-the-art rendering quality at real-time speeds but suffers from "model bloat" - a large number of redunda...
By Zi-Ming Wang, Kai-Wen Duan, Kowei Huang, Akihiro Sugimoto, Shang-Hong Lai
The paper introduces TruncGradGS, a piecewise truncated gradient approach that mitigates gradient vanishing in 3D Gaussian Splatting, enhancing optimization stability and robustness to initializations. It demonstrates consistent improvements over random and COLMAP initializations in both static and dynamic settings. Additionally, the authors highlight limitations of existing dynamic scene benchmarks and present a new synthetic dataset for evaluating dynamic Gaussian Splatting.
By Theo Morales, Nhat-Quynh Le-Pham, Robin Atkins, Binh-Son Hua
KISS-GS is a modular compression pipeline for 3D Gaussian Splatting (3DGS) scenes that separates compression from training. It first compacts a vanilla 3DGS scene by 15.7× using state‑of‑the‑art pruning, then encodes the result into the SOG‑XT image‑based format, achieving an additional 6.6× reduction. Optional encoding‑aware fine‑tuning can further cut the size by 2.2×, yielding total reductions of 85× to 319× on standard benchmarks while enabling web‑native decoding.
By Wieland Morgenstern, Friedrich Elias Branschke, Florian Fleischmann, Adrian Szatmari, Paul Schlack, Florian Barthel, Peter Eisert, Anna Hilsmann