Stereo4D：从互联网立体视频中学习事物的3D运动
Stereo4D: Learning How Things Move in 3D from Internet Stereo Videos
AI总结：
提出Stereo4D系统，从互联网立体视频中挖掘高质量4D重建，生成大规模世界一致性3D点云数据，训练DUSt3R变体实现真实图像对的3D结构与运动预测。
摘要：
从图像中学习理解动态3D场景对于从机器人到场景重建等应用至关重要。然而，不同于其他通过大规模监督训练实现快速进展的问题，由于获取真实标注存在根本困难，直接监督恢复3D运动的方法仍具挑战。我们提出一种从互联网立体广角视频中挖掘高质量4D重建的系统。该系统将相机位姿估计、立体深度估计和时间跟踪方法的输出融合并过滤，生成高质量的动态3D重建。我们利用该方法生成大规模数据，形式为具有长期运动轨迹的世界一致性伪度量3D点云。我们通过训练DUSt3R的变体来从真实图像对中预测结构和3D运动，展示了该数据的实用性，表明在重建数据上训练能泛化至多样的真实场景。
英文摘要：
Learning to understand dynamic 3D scenes from imagery is crucial for applications ranging from robotics to scene reconstruction. Yet, unlike other problems where large-scale supervised training has enabled rapid progress, directly supervising methods for recovering 3D motion remains challenging due to the fundamental difficulty of obtaining ground truth annotations. We present a system for mining high-quality 4D reconstructions from internet stereoscopic, wide-angle videos. Our system fuses and filters the outputs of camera pose estimation, stereo depth estimation, and temporal tracking methods into high-quality dynamic 3D reconstructions. We use this method to generate large-scale data in the form of world-consistent, pseudo-metric 3D point clouds with long-term motion trajectories. We demonstrate the utility of this data by training a variant of DUSt3R to predict structure and 3D motion from real-world image pairs, showing that training on our reconstructed data enables generalization to diverse real-world scenes. Project page and data at: https://stereo4d.github.io
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。
