PTC-Depth：具有时序一致性的位姿精化单目深度估计
PTC-Depth: Pose-Refined Monocular Depth Estimation with Temporal Consistency
利用轮式里程计和光流三角化得到的稀疏深度，对预训练深度基础模型的相对深度做递归贝叶斯尺度校正，为视觉研究者提供了一种在连续帧间保持深度时序一致性的轻量级后处理框架。
原文依据 The sparse depth estimates are used to update a recursive Bayesian estimate of the metric scale, which is then applied to rescale the relative depth predicted by a pre-trained depth estimation foundation model.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论单目深度估计（MDE）已被广泛应用于自动驾驶车辆和移动机器人的感知系统中。然而，现有方法往往难以在连续帧之间保持深度估计的时序一致性。这种不一致不仅会导致抖动，而且在深度范围突然变化时还可能导致估计失败。为解决这些挑战，本文提出了一种一致性感知的单目深度估计框架，利用移动机器人的轮式里程计来实现随时间稳定且连贯的深度预测。具体而言，我们利用连续帧之间的光流，通过三角化估计相机位姿和稀疏深度。稀疏深度估计被用于更新对度量尺度的递归贝叶斯估计，然后将其应用于重新缩放由预训练深度估计基础模型预测的相对深度。所提方法在 KITTI、TartanAir、MS2 以及我们自己的数据集上进行了评估，展示了稳健且准确的深度估计性能。
英文原文摘要 1066 字符
Monocular depth estimation (MDE) has been widely adopted in the perception systems of autonomous vehicles and mobile robots. However, existing approaches often struggle to maintain temporal consistency in depth estimation across consecutive frames. This inconsistency not only causes jitter but can also lead to estimation failures when the depth range changes abruptly. To address these challenges, this paper proposes a consistency-aware monocular depth estimation framework that leverages wheel odometry from a mobile robot to achieve stable and coherent depth predictions over time. Specifically, we estimate camera pose and sparse depth from triangulation using optical flow between consecutive frames. The sparse depth estimates are used to update a recursive Bayesian estimate of the metric scale, which is then applied to rescale the relative depth predicted by a pre-trained depth estimation foundation model. The proposed method is evaluated on the KITTI, TartanAir, MS2, and our own dataset, demonstrating robust and accurate depth estimation performance.