We consider the problem of tracking the 6D pose of a moving RGB-D camera in a neural scene representation. Different such representations have recently emerged, and we investigate the suitability of them for the task of camera tracking. In particular, we propose to track an RGB-D camera using a signed distance field-based representation and show that compared to density-based representations, tracking can be sped up, which enables more robust and accurate pose estimates when computation time is limited.
Cite
@article{arxiv.2205.02079,
title = {SDF-based RGB-D Camera Tracking in Neural Scene Representations},
author = {Leonard Bruns and Fereidoon Zangeneh and Patric Jensfelt},
journal= {arXiv preprint arXiv:2205.02079},
year = {2022}
}
Comments
Accepted to the "Motion Planning with Implicit Neural Representations of Geometry" Workshop at ICRA 2022