Free-Moving Object Reconstruction and Pose Estimation with Virtual Camera
This addresses the challenge of 3D reconstruction for robotics or AR/VR applications, but it is incremental as it builds on existing implicit neural representations.
The authors tackled the problem of reconstructing free-moving objects from monocular RGB video without relying on prior assumptions, achieving performance on par with recent methods that use prior information.
We propose an approach for reconstructing free-moving object from a monocular RGB video. Most existing methods either assume scene prior, hand pose prior, object category pose prior, or rely on local optimization with multiple sequence segments. We propose a method that allows free interaction with the object in front of a moving camera without relying on any prior, and optimizes the sequence globally without any segments. We progressively optimize the object shape and pose simultaneously based on an implicit neural representation. A key aspect of our method is a virtual camera system that reduces the search space of the optimization significantly. We evaluate our method on the standard HO3D dataset and a collection of egocentric RGB sequences captured with a head-mounted device. We demonstrate that our approach outperforms most methods significantly, and is on par with recent techniques that assume prior information.