BundleSDF: 未知物体のニューラル6自由度トラッキングと3D再構成
BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects
単眼RGBD動画から未知の物体の6自由度姿勢を追跡しながら、ニューラル物体場を用いてその3D形状と外観を同時に再構成する手法を提案。
著者: Bowen Wen, Jonathan Tremblay, Valts Blukis, Stephen Tyree, Thomas Muller, Alex Evans, Dieter Fox, Jan Kautz, Stan Birchfield
分類: cs.CV, cs.AI, cs.GR, cs.RO
原文アブストラクト
We present a near real-time method for 6-DoF tracking of an unknown object from a monocular RGBD video sequence, while simultaneously performing neural 3D reconstruction of the object. Our method works for arbitrary rigid objects, even when visual texture is largely absent. The object is assumed to be segmented in the first frame only. No additional information is required, and no assumption is made about the interaction agent. Key to our method is a Neural Object Field that is learned concurrently with a pose graph optimization process in order to robustly accumulate information into a consistent 3D representation capturing both geometry and appearance. A dynamic pool of posed memory frames is automatically maintained to facilitate communication between these threads. Our approach handles challenging sequences with large pose changes, partial and full occlusion, untextured surfaces, and specular highlights. We show results on HO3D, YCBInEOAT, and BEHAVE datasets, demonstrating that our method significantly outperforms existing approaches. Project page: https://bundlesdf.github.io
