arXiv AI July 2, 2026 By Kangmin Seo, Sangeek Hyun, MinKyu Lee, Jae-Pil Heo

Improving Sparse-View 3DGS Generalization via Flat Minima Optimization

arXiv:2607. 00885v1 Announce Type: cross Abstract: Recent advances in neural rendering have established 3D Gaussian Splatting (3DGS) as a highly efficient representation for novel view synthesis, enabling fast training and real-time rendering with strong fidelity.

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 AI.

arXiv Computer Vision

3d ago

FrontierGS: Progressive View-Space Frontier Expansion for Sparse 3D Gaussian Splatting

arXiv:2511.16030v3 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) enables efficient, high-fidelity novel view synthesis, yet its performance degrades severely under sparse-view supervi...

By Zijian Wu, Mingfeng Jiang, Zidian Lin, Ying Song, Ziqian Lu, Qun Wu, Hanjie Ma

benchmarks

arXiv Machine Learning

Jun 25

SparseGS: Sparse View Synthesis using 3D Gaussian Splatting

arXiv:2312. 00206v4 Announce Type: replace-cross Abstract: 3D Gaussian Splatting (3DGS) has recently enabled real-time rendering of unbounded 3D scenes for novel view synthesis.

By Haolin Xiong, Sairisheek Muttukuru, Hanyuan Xiao, Rishi Upadhyay, Pradyumna Chari, Yajie Zhao, Achuta Kadambi

efficiency

arXiv AI

6d ago

Spackle: Completing Large View Single Image NVS with Adaptive Gaussians

Spackle is a lightweight residual learning framework designed to improve large-view single-image novel view synthesis (NVS) by mitigating capacity competition in hybrid decoupled systems that combine 3D Gaussian Splatting (3DGS) and diffusion models. It operates in three stages: predicting base 3DGS attributes, automatically identifying poorly reconstructed regions, and learning a residual 3DGS focused on those areas. During inference, Spackle merges the baseline and augmented Gaussians to produce high-fidelity novel views, achieving state‑of‑the‑art performance on large-view-deviation cases.

By Xuanzhi Liu, Yuhe Zhou, Xinyi Wu, Zhenyao Wu, Jinghao Chen, Ruize Han, Song Wang

diffusion benchmarks

arXiv Computer Vision

Aug 25

Seeing the Unseen: Semantic-in-Gaussian for Sparse-View 3D Generalization

arXiv:2608.22740v1 Announce Type: new Abstract: Generalizable 3D Gaussian Splatting (G-3DGS) has emerged as a promising approach for novel view synthesis undersparse-view settings. However, existing...

By Zeyang Bai, Yunpeng Wang, Yunbiao Wang, Jun Xiao

llms rag benchmarks

arXiv Computer Vision

Sep 24

Learn2Splat: Extending the Horizon of Learned 3DGS Optimization

arXiv:2605.15760v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) optimization is most commonly performed using general-purpose first-order optimizers such as Adam or SGD. Although rob...

By Naama Pearl, Stefano Esposito, Haofei Xu, Amit Peleg, Patricia Gschossmann, Lorenzo Porzi, Peter Kontschieder, Gerard Pons-Moll, Andreas Geiger

arXiv Computer Vision

Sep 22

D3GS: Depth, DINO, and RGB Diffusion Co-Guided 3D Gaussian Splatting for Sparse-View Reconstruction

arXiv:2609.22941v1 Announce Type: new Abstract: Novel view synthesis from sparse inputs remains challenging for 3D Gaussian Splatting (3DGS) due to ambiguous geometry, cross-view inconsistency, and m...

By Yunqi Gao, Zhanfeng Liao, Hanzhang Tu, Zhaoqi Su, Guoqing Zheng, Songtao Wang, Hongwen Zhang, Zhou Xue, Leyuan Liu, Yebin Liu

llms diffusion benchmarks

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Related stories

FrontierGS: Progressive View-Space Frontier Expansion for Sparse 3D Gaussian Splatting

SparseGS: Sparse View Synthesis using 3D Gaussian Splatting

Spackle: Completing Large View Single Image NVS with Adaptive Gaussians

Seeing the Unseen: Semantic-in-Gaussian for Sparse-View 3D Generalization

Learn2Splat: Extending the Horizon of Learned 3DGS Optimization

D3GS: Depth, DINO, and RGB Diffusion Co-Guided 3D Gaussian Splatting for Sparse-View Reconstruction