SmallGS：基于高斯泼溅的小基线视频相机位姿估计
SmallGS: Gaussian Splatting-based Camera Pose Estimation for Small-Baseline Videos
AI总结：
SmallGS利用高斯泼溅为小基线视频提供稳定场景参考，融合DINOv2特征增强鲁棒性，无需强视差即可估计相机位姿，在TUM-Dynamics上优于MonST3R和DORID-SLAM。
摘要：
具有小基线运动的动态视频在日常生活中无处不在，尤其是在社交媒体上。然而，由于特征模糊、漂移累积和三角化约束不足，这些视频对现有的位姿估计框架构成了挑战。高斯泼溅（Gaussian splatting）为场景维护显式表示，在视点变化较小时能够提供可靠的新视图光栅化。受此启发，我们提出了SmallGS，一个专门为小基线视频设计的相机位姿估计框架。SmallGS使用高斯泼溅优化顺序相机位姿，该技术从每个视频片段的第一帧重建场景，为其余帧提供稳定参考。在有限视点差异内，高斯泼溅的时间一致性降低了对传统相机位姿估计中足够深度变化的需求。我们进一步将预训练的鲁棒视觉特征（如DINOv2）融入高斯泼溅，其中高维特征图渲染增强了相机位姿估计的鲁棒性。通过冻结高斯泼溅并基于光栅化特征优化相机视点，SmallGS无需显式特征对应或强视差运动即可有效学习相机位姿。我们在TUM-Dynamics序列上验证了SmallGS在小基线视频中的有效性，与MonST3R和DORID-SLAM相比，在动态场景的小基线视频中实现了令人印象深刻的相机位姿估计精度。我们的项目页面位于：https://yuxinyao620.github.io/SmallGS
英文摘要：
Dynamic videos with small baseline motions are ubiquitous in daily life, especially on social media. However, these videos present a challenge to existing pose estimation frameworks due to ambiguous features, drift accumulation, and insufficient triangulation constraints. Gaussian splatting, which maintains an explicit representation for scenes, provides a reliable novel view rasterization when the viewpoint change is small. Inspired by this, we propose SmallGS, a camera pose estimation framework that is specifically designed for small-baseline videos. SmallGS optimizes sequential camera poses using Gaussian splatting, which reconstructs the scene from the first frame in each video segment to provide a stable reference for the rest. The temporal consistency of Gaussian splatting within limited viewpoint differences reduced the requirement of sufficient depth variations in traditional camera pose estimation. We further incorporate pretrained robust visual features, e.g. DINOv2, into Gaussian splatting, where high-dimensional feature map rendering enhances the robustness of camera pose estimation. By freezing the Gaussian splatting and optimizing camera viewpoints based on rasterized features, SmallGS effectively learns camera poses without requiring explicit feature correspondences or strong parallax motion. We verify the effectiveness of SmallGS in small-baseline videos in TUM-Dynamics sequences, which achieves impressive accuracy in camera pose estimation compared to MonST3R and DORID-SLAM for small-baseline videos in dynamic scenes. Our project page is at: https://yuxinyao620.github.io/SmallGS
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。
