Adaptive and Temporally Consistent Gaussian Surfels for Multi-view Dynamic Reconstruction

3D Gaussian Splatting has recently achieved notable success in novel view synthesis for dynamic scenes and ge-ometry reconstruction in static scenes. Building on these advancements, early methods have been developed for dy-namic surface reconstruction by globally optimizing entire sequences. However, reconstructing dynamic scenes with significant topology changes, emerging or disappearing ob-jects, and rapid movements remains a substantial chal-lenge, particularly for long sequences. To address these issues, we propose AT-GS, a novel method for reconstructing high-quality dynamic surfaces from multi-view videos through per-frame incremental optimization. To avoid local minima across frames, we introduce a unified and adaptive gradient-aware densification strategy that integrates the strengths of conventional cloning and splitting techniques. Additionally, we reduce temporal jittering in dy-namic surfaces by ensuring consistency in curvature maps across consecutive frames. Our method achieves superior accuracy and temporal coherence in dynamic surface re-construction, delivering high-fidelity space-time novel view synthesis, even in complex and challenging scenes. Extensive experiments on diverse multi-view video datasets demonstrate the effectiveness of our approach, showing clear advantages over baseline methods. Project page: https://fraunhoferhhi.github.io/AT-GS