Pose-free 3D Gaussian splatting via shape-ray estimation

• URL: http://arxiv.org/abs/2505.22978v3
• Date: Tue, 21 Oct 2025 11:48:43 GMT
• Title: Pose-free 3D Gaussian splatting via shape-ray estimation
• Authors: Youngju Na, Taeyeon Kim, Jumin Lee, Kyu Beom Han, Woo Jae Kim, Sung-eui Yoon,
• Abstract summary: We introduce SHARE, a pose-free, feed-forward Gaussian splatting framework.<n>Instead of relying on explicit 3D transformations, SHARE builds a pose-aware canonical volume representation.<n>Experiments on diverse real-world datasets show that our method achieves robust performance.
• Score: 22.44197443080198
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: While generalizable 3D Gaussian splatting enables efficient, high-quality rendering of unseen scenes, it heavily depends on precise camera poses for accurate geometry. In real-world scenarios, obtaining accurate poses is challenging, leading to noisy pose estimates and geometric misalignments. To address this, we introduce SHARE, a pose-free, feed-forward Gaussian splatting framework that overcomes these ambiguities by joint shape and camera rays estimation. Instead of relying on explicit 3D transformations, SHARE builds a pose-aware canonical volume representation that seamlessly integrates multi-view information, reducing misalignment caused by inaccurate pose estimates. Additionally, anchor-aligned Gaussian prediction enhances scene reconstruction by refining local geometry around coarse anchors, allowing for more precise Gaussian placement. Extensive experiments on diverse real-world datasets show that our method achieves robust performance in pose-free generalizable Gaussian splatting. Code is avilable at https://github.com/youngju-na/SHARE

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