We propose a novel online, point-based 3D reconstruction method from posed monocular RGB videos. Our model maintains a global point cloud representation of the scene, continuously updating the features and 3D locations of points as new images are observed. It expands the point cloud with newly detected points while carefully removing redundancies. The point cloud updates and depth predictions for new points are achieved through a novel ray-based 2D-3D feature matching technique, which is robust against errors in previous point position predictions. In contrast to offline methods, our approach processes infinite-length sequences and provides real-time updates. Additionally, the point cloud imposes no pre-defined resolution or scene size constraints, and its unified global representation ensures view consistency across perspectives. Experiments on the ScanNet dataset show that our method achieves state-of-the-art quality among online MVS approaches. Project page: https://arthurhero.github.io/projects/pointrecon

本研究解决了从单摄像头RGB视频进行在线3D重建的问题，提出了一种新的点云表示方式。通过射线基础的2D-3D特征匹配技术，实时更新点云和深度预测，使其在保持高质量的同时，避免了以前点预测错误的影响。实验结果表明，该方法在在线多视图立体（MVS）任务上达到了最先进的性能。

PointRecon：基于射线的在线点云3D重建方法