Look Gauss, No Pose: Novel View Synthesis using Gaussian Splatting without Accurate Pose Initialization

• URL: http://arxiv.org/abs/2410.08743v1
• Date: Fri, 11 Oct 2024 12:01:15 GMT
• Title: Look Gauss, No Pose: Novel View Synthesis using Gaussian Splatting without Accurate Pose Initialization
• Authors: Christian Schmidt, Jens Piekenbrinck, Bastian Leibe,
• Abstract summary: 3D Gaussian Splatting has emerged as a powerful tool for fast and accurate novel-view synthesis from a set of posed input images. We propose an extension to the 3D Gaussian Splatting framework by optimizing the extrinsic camera parameters with respect to photometric residuals. We show results on real-world scenes and complex trajectories through simulated environments.
• Score: 11.418632671254564
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D Gaussian Splatting has recently emerged as a powerful tool for fast and accurate novel-view synthesis from a set of posed input images. However, like most novel-view synthesis approaches, it relies on accurate camera pose information, limiting its applicability in real-world scenarios where acquiring accurate camera poses can be challenging or even impossible. We propose an extension to the 3D Gaussian Splatting framework by optimizing the extrinsic camera parameters with respect to photometric residuals. We derive the analytical gradients and integrate their computation with the existing high-performance CUDA implementation. This enables downstream tasks such as 6-DoF camera pose estimation as well as joint reconstruction and camera refinement. In particular, we achieve rapid convergence and high accuracy for pose estimation on real-world scenes. Our method enables fast reconstruction of 3D scenes without requiring accurate pose information by jointly optimizing geometry and camera poses, while achieving state-of-the-art results in novel-view synthesis. Our approach is considerably faster to optimize than most competing methods, and several times faster in rendering. We show results on real-world scenes and complex trajectories through simulated environments, achieving state-of-the-art results on LLFF while reducing runtime by two to four times compared to the most efficient competing method. Source code will be available at https://github.com/Schmiddo/noposegs .

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