用射线匹配实现实时3D点云重建，不依赖预设分辨率
PointRecon: Online Point-based 3D Reconstruction via Ray-based 2D-3D Matching
- 基于射线的2D-3D特征匹配，提升点位置更新精度
- 在ScanNet上达到在线多视图立体视觉的领先效果
- 适合需要实时更新、无尺寸限制的3D重建场景
我们提出一种从带位姿的单目RGB视频中进行在线点云式3D重建的新方法。模型维护全局点云表示，随新图像输入持续更新点的特征与3D位置，并通过新检测点扩展点云，同时谨慎剔除冗余。点云更新与新点深度预测通过一种新颖的基于射线的2D-3D特征匹配技术实现，对先前点位置预测误差具有鲁棒性。相比离线方法，本方法可处理无限长度序列并提供实时更新。点云无预定义分辨率或场景大小限制，统一的全局表示确保多视角一致性。在ScanNet数据集上的实验表明，该方法在在线多视图立体视觉（MVS）方法中达到相当的重建质量。
原文摘要 · Abstract (English)
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 the 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 comparable quality among online MVS approaches. Project page: https://arthurhero.github.io/projects/pointrecon
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