SDF-based RGB-D Camera Tracking in Neural Scene Representations

Abstract

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.

Accepted to the "Motion Planning with Implicit Neural Representations of Geometry" Workshop at ICRA 2022