Year: 2023
Type: conference-paper
Abstract: We propose a novel geometric and photometric 3D mapping pipeline for accurate and real-time scene reconstruction from casually taken monocular images. To achieve this, we leverage recent advances in dense monocular SLAM and real-time hierarchical volumetric neural radiance fields. Our insight is that dense monocular SLAM provides the right information to fit a neural radiance field of the scene in real-time, by providing accurate pose estimates and depth-maps with associated uncertainty. Our proposed pipeline achieves better geometric and photometric accuracy than competing approaches (up to 178% better PSNR and 75% better L1 depth), while working in real-time and using only monocular images.
Authors: Antoni Rosinol, John J. Leonard, Luca Carlone
Institution: Massachusetts Institute of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4389665446