CL-Splats: Continual Learning of Gaussian Splatting with Local Optimization

• URL: http://arxiv.org/abs/2506.21117v1
• Date: Thu, 26 Jun 2025 09:32:37 GMT
• Title: CL-Splats: Continual Learning of Gaussian Splatting with Local Optimization
• Authors: Jan Ackermann, Jonas Kulhanek, Shengqu Cai, Haofei Xu, Marc Pollefeys, Gordon Wetzstein, Leonidas Guibas, Songyou Peng,
• Abstract summary: This paper introduces CL-Splats, which incrementally updates 3D representations from sparse scene captures.<n> CL-Splats integrates a robust change-detection module that segments updated and static components within the scene.<n>Our experiments demonstrate that CL-Splats achieves efficient updates with improved reconstruction quality over the state-of-the-art.
• Score: 68.89159693946685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In dynamic 3D environments, accurately updating scene representations over time is crucial for applications in robotics, mixed reality, and embodied AI. As scenes evolve, efficient methods to incorporate changes are needed to maintain up-to-date, high-quality reconstructions without the computational overhead of re-optimizing the entire scene. This paper introduces CL-Splats, which incrementally updates Gaussian splatting-based 3D representations from sparse scene captures. CL-Splats integrates a robust change-detection module that segments updated and static components within the scene, enabling focused, local optimization that avoids unnecessary re-computation. Moreover, CL-Splats supports storing and recovering previous scene states, facilitating temporal segmentation and new scene-analysis applications. Our extensive experiments demonstrate that CL-Splats achieves efficient updates with improved reconstruction quality over the state-of-the-art. This establishes a robust foundation for future real-time adaptation in 3D scene reconstruction tasks.

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