arXiv Machine Learning June 30, 2026 By Zhican Wang, Guanghui He, Lingjun Gao, Dantong Liu, Shell Xu Hu, Chen Zhang, Zhuoran Song, Nicholas Lane, Hongxiang Fan

Efficient 3D Gaussian Splatting with Axis-Shared Rasterization and Order-independent Transmittance

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 21

CaT-GS: Efficient 3DGS Rendering for Large Scale Scenes via Inter-frame Caching and Tile Scheduling

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

benchmarks

arXiv Computer Vision

Aug 25

Fast and Compact 3D Gaussian Splatting with Polarized Opacity Prior

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

efficiency benchmarks

arXiv AI

Sep 10

From Splats to Silicon: Rethinking Computational Efficiency of 3DGS

arXiv:2609.06157v1 Announce Type: cross Abstract: 3D Gaussian splatting (3DGS) represents scenes with explicit primitives and supports real-time novel-view synthesis, yet its system efficiency varies...

By Minnan Pei, Qiwei Dong, Yihan Zhou, Gang Li, Yuchen Zhu, Wenju Zhao, Zhongtian Long, Siting Wang, Peisong Wang, Jian Cheng

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

arXiv Machine Learning

Jul 7

Provable Pruning for Efficient 3D Gaussian Splatting via Coresets

arXiv:2607. 02721v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) enables high-quality real-time novel-view synthesis, but practical scenes often contain millions of Gaussians, making compression essential for deployment on limited hardware.

By Waseem Mousa, Alaa Maalouf

fine-tuning efficiency benchmarks

Hugging Face Trending Papers

Jun 17

Splaxel: Efficient Distributed Training of 3D Gaussian Splatting for Large-scale Scene Reconstruction via Pixel-level Communication

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.