我们提出了一种近实时的方法，可以从单目RGBD视频序列中跟踪未知物体的6自由度运动，并同时执行神经3D重建。我们的方法适用于任意刚性物体，即使视觉纹理基本不存在。假定在第一帧中分割出物体。不需要额外的信息，并且不对交互代理做出任何假设。我们方法的关键是学习一个神经对象场，与姿势图优化过程同时学习，从而将信息稳健地积累到一致的3D表示中，捕捉几何和外观信息。自动维护一个姿势记忆帧的动态池，以促进这些线程之间的通信。我们的方法可以处理具有大的姿态变化、部分和完全遮挡、无纹理表面和高光反射的具有挑战性的序列。我们在HO3D、YCBInEOAT和BEHAVE数据集上展示了结果，证明我们的方法明显优于现有方法。
标题：BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects
作者：Bowen Wen, Jonathan Tremblay, Valts Blukis, Stephen Tyree, Thomas Muller, Alex Evans, Dieter Fox, Jan Kautz, Stan Birchfield
论文：https://papers.labml.ai/api/v1/redirect/pdf?paper_key=fe4f5688cc4911edb95839eec3084ddd
项目：https://bundlesdf.github.io/
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