NARUTO: Neural Active Reconstruction from Uncertain Target Observations

arXiv:2402.18771v1 Announce Type: new
Abstract: We present NARUTO, a neural active reconstruction system that combines a hybrid neural representation with uncertainty learning, enabling high-fidelity surface reconstruction. Our approach leverages a multi-resolution hash-grid as the mapping backbone, chosen for its exceptional convergence speed and capacity to capture high-frequency local features.The centerpiece of our work is the incorporation of an uncertainty learning module that dynamically quantifies reconstruction uncertainty while actively reconstructing the environment. By harnessing learned uncertainty, we propose a novel uncertainty …

abstract arxiv capacity convergence cs.cv cs.ro enabling features fidelity grid hash hybrid mapping representation speed surface type uncertain uncertainty