迈向更好的鲁棒性：面向任意长视频的无姿态三维高斯泼溅
Towards Better Robustness: Pose-Free 3D Gaussian Splatting for Arbitrarily Long Videos
AI总结：
本文提出Rob-GS框架，利用视频连续性和分段优化策略，实现任意长视频的无姿态三维高斯泼溅，提升鲁棒性并超越现有方法。
摘要：
三维高斯泼溅（3DGS）因其高效性和高保真渲染而成为一种强大的表示方法。3DGS的训练需要每个输入视图的已知相机姿态，通常通过运动恢复结构（SfM）流程获取。开创性工作试图放宽这一限制，但在处理具有复杂相机轨迹的长序列时仍面临困难。在本文中，我们提出Rob-GS，一个鲁棒的框架，用于逐步估计相机姿态并优化任意长视频输入的3DGS。具体而言，通过利用视频固有的连续性，我们设计了一种相邻姿态跟踪方法，以确保连续帧之间的稳定姿态估计。为了处理任意长的输入，我们提出了一种高斯可见性保留检查策略，以自适应地将视频序列分割成多个片段并分别优化。在Tanks and Temples、ScanNet以及自采集数据集上的大量实验表明，Rob-GS优于现有最先进方法。
英文摘要：
3D Gaussian Splatting (3DGS) has emerged as a powerful representation due to its efficiency and high-fidelity rendering. 3DGS training requires a known camera pose for each input view, typically obtained by Structure-from-Motion (SfM) pipelines. Pioneering works have attempted to relax this restriction but still face difficulties when handling long sequences with complex camera trajectories. In this paper, we propose Rob-GS, a robust framework to progressively estimate camera poses and optimize 3DGS for arbitrarily long video inputs. In particular, by leveraging the inherent continuity of videos, we design an adjacent pose tracking method to ensure stable pose estimation between consecutive frames. To handle arbitrarily long inputs, we propose a Gaussian visibility retention check strategy to adaptively split the video sequence into several segments and optimize them separately. Extensive experiments on Tanks and Temples, ScanNet, and a self-captured dataset show that Rob-GS outperforms the state-of-the-arts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
