用GPU加速实现高精度离线相机位姿估计
CuSfM: CUDA-Accelerated Structure-from-Motion
- 基于CUDA的并行计算，高效运行高精度特征提取器
- 相比COLMAP，处理速度更快且精度显著提升
- 适合需要高精度地图的机器人、自动驾驶离线建模
高效准确的相机位姿估计是自主导航、机器人感知和虚拟仿真系统中密集重建的基础。本文提出cuSfM，一种基于CUDA加速的离线SfM系统，利用GPU并行化能力，高效运行计算量大但精度高的特征提取器，在生成全面且无冗余的数据关联基础上，实现精确的相机位姿估计与全局一致的建图。系统支持位姿优化、建图、先验地图定位及外参精调。专为离线处理设计，可充分释放计算资源以最大化精度。实验表明，cuSfM在多种测试场景下相比广泛使用的COLMAP方法，显著提升了处理速度与精度，同时保持了离线SfM应用所必需的高精度与全局一致性。系统以开源Python封装形式发布，名为PyCuSfM，可在https://github.com/nvidia-isaac/pyCuSFM获取，便于计算机视觉与机器人领域的研究与应用。
原文摘要 · Abstract (English)
Efficient and accurate camera pose estimation forms the foundational requirement for dense reconstruction in autonomous navigation, robotic perception, and virtual simulation systems. This paper addresses the challenge via cuSfM, a CUDA-accelerated offline Structure-from-Motion system that leverages GPU parallelization to efficiently employ computationally intensive yet highly accurate feature extractors, generating comprehensive and non-redundant data associations for precise camera pose estimation and globally consistent mapping. The system supports pose optimization, mapping, prior-map localization, and extrinsic refinement. It is designed for offline processing, where computational resources can be fully utilized to maximize accuracy. Experimental results demonstrate that cuSfM achieves significantly improved accuracy and processing speed compared to the widely used COLMAP method across various testing scenarios, while maintaining the high precision and global consistency essential for offline SfM applications. The system is released as an open-source Python wrapper implementation, PyCuSfM, available at https://github.com/nvidia-isaac/pyCuSFM, to facilitate research and applications in computer vision and robotics.
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