SaLon3R: Structure-aware Long-term Generalizable 3D Reconstruction from Unposed Images Article Swipe

MAKE IMPACT

Jiaxin Guo , Tongfan Guan , Wenzhen Dong , Wenzhao Zheng , Wenting Wang , Yue Wang , Yeung Yam , Yun‐Hui Liu · YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2510.15072

Recent advances in 3D Gaussian Splatting (3DGS) have enabled generalizable, on-the-fly reconstruction of sequential input views. However, existing methods often predict per-pixel Gaussians and combine Gaussians from all views as the scene representation, leading to substantial redundancies and geometric inconsistencies in long-duration video sequences. To address this, we propose SaLon3R, a novel framework for Structure-aware, Long-term 3DGS Reconstruction. To our best knowledge, SaLon3R is the first online generalizable GS method capable of reconstructing over 50 views in over 10 FPS, with 50% to 90% redundancy removal. Our method introduces compact anchor primitives to eliminate redundancy through differentiable saliency-aware Gaussian quantization, coupled with a 3D Point Transformer that refines anchor attributes and saliency to resolve cross-frame geometric and photometric inconsistencies. Specifically, we first leverage a 3D reconstruction backbone to predict dense per-pixel Gaussians and a saliency map encoding regional geometric complexity. Redundant Gaussians are compressed into compact anchors by prioritizing high-complexity regions. The 3D Point Transformer then learns spatial structural priors in 3D space from training data to refine anchor attributes and saliency, enabling regionally adaptive Gaussian decoding for geometric fidelity. Without known camera parameters or test-time optimization, our approach effectively resolves artifacts and prunes the redundant 3DGS in a single feed-forward pass. Experiments on multiple datasets demonstrate our state-of-the-art performance on both novel view synthesis and depth estimation, demonstrating superior efficiency, robustness, and generalization ability for long-term generalizable 3D reconstruction. Project Page: https://wrld.github.io/SaLon3R/.

Type

preprint

Landing Page

http://arxiv.org/abs/2510.15072

PDF

https://arxiv.org/pdf/2510.15072

OA Status

green

OpenAlex ID

https://openalex.org/W4415868113

Raw OpenAlex JSON

OpenAlex ID

https://openalex.org/W4415868113

Canonical identifier for this work in OpenAlex

DOI

https://doi.org/10.48550/arxiv.2510.15072

Digital Object Identifier

Title

SaLon3R: Structure-aware Long-term Generalizable 3D Reconstruction from Unposed Images

Work title

Type

preprint

OpenAlex work type

Publication year

2025

Year of publication

Publication date

2025-10-16

Full publication date if available

Authors

Jiaxin Guo, Tongfan Guan, Wenzhen Dong, Wenzhao Zheng, Wenting Wang, Yue Wang, Yeung Yam, Yun‐Hui Liu

List of authors in order

Landing page

https://arxiv.org/abs/2510.15072

Publisher landing page

PDF URL

https://arxiv.org/pdf/2510.15072

Direct link to full text PDF

Open access

Yes

Whether a free full text is available

OA status

green

Open access status per OpenAlex

OA URL

https://arxiv.org/pdf/2510.15072

Direct OA link when available

Cited by

0

Total citation count in OpenAlex

Full payload