We present a robust and accurate depth refinement system, named GeoRefine,
for geometrically-consistent dense mapping from monocular sequences. GeoRefine
consists of three modules: a hybrid SLAM module using learning-based priors, an
online depth refinement module leveraging self-supervision, and a global
mapping module via TSDF fusion. The proposed system is online by design and
achieves great robustness and accuracy via: (i) a robustified hybrid SLAM that
incorporates learning-based optical flow and/or depth; (ii) self-supervised
losses that leverage SLAM outputs and enforce long-term geometric consistency;
(iii) careful system design that avoids degenerate cases in online depth
refinement. We extensively evaluate GeoRefine on multiple public datasets and
reach as low as $5\%$ absolute relative depth errors.

GeoRefine 是一种深度细化系统，用于几何一致的稠密映射，包含三个模块：SLAM 模块，在线深度细化模块和全局映射模块。系统设计严谨，通过使用基于学习的先验知识的混合 SLAM 模块、自监督损失和避免在线深度细化退化情况的精心系统设计，实现了良好的鲁棒性和精度。在多个公共数据集上广泛评估 GeoRefine，达到了低至 5％的绝对相对深度误差。

GeoRefine：用于精确密集地图的自监督在线深度细化

GeoRefine: Self-Supervised Online Depth Refinement for Accurate Dense  Mapping

The self-supervised learning of depth and pose from monocular sequences
provides an attractive solution by using the photometric consistency of nearby
frames as it depends much less on the ground-truth data. In this paper, we
address the issue when previous assumptions of the self-supervised approaches
are violated due to the dynamic nature of real-world scenes. Different from
handling the noise as uncertainty, our key idea is to incorporate more robust
geometric quantities and enforce internal consistency in the temporal image
sequence. As demonstrated on commonly used benchmark datasets, the proposed
method substantially improves the state-of-the-art methods on both depth and
relative pose estimation for monocular image sequences, without adding
inference overhead.

本文介绍了一种通过自监督学习获得单目序列深度和姿态的方法，该方法不需要基础数据的支持，增加了更强的几何量和时间图像序列内部一致性的约束条件，能够显著提高单目图像序列深度和相对姿态估计的性能。