SHARE: Pose-Free 3D Gaussian Splatting Framework for Robust Scene Reconstruction
By
PaulHoule
Toasted to a respectable shade. No regrets, no crumbs left.
Summary
Researchers introduce SHARE, a pose-free 3D Gaussian splatting framework that addresses the challenge of inaccurate camera poses in real-world scenarios. The method uses joint shape and camera rays estimation to create a pose-aware canonical volume representation, reducing geometric misalignments. It also employs anchor-aligned Gaussian prediction to refine local geometry for more precise scene reconstruction. The approach shows robust performance on diverse real-world datasets without requiring precise camera poses.
Key quotes
· 4 pulledWhile generalizable 3D Gaussian splatting enables efficient, high-quality rendering of unseen scenes, it heavily depends on precise camera poses for accurate geometry.
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.
Extensive experiments on diverse real-world datasets show that our method achieves robust performance in pose-free generalizable Gaussian splatting.
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