人工智能
单眼
光辉
计算机视觉
杠杆(统计)
计算机科学
同时定位和映射
管道(软件)
遥感
机器人
地质学
移动机器人
程序设计语言
作者
Antoni Rosinol,John J. Leonard,Luca Carlone
标识
DOI:10.1109/iros55552.2023.10341922
摘要
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.
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