Robust Consistent Video Depth Estimation

Kopf, Johannes; Xiao, Rong; Huang, Jia-Bin

doi:10.48550/arxiv.2012.05901

Cited by 1 publication

(2 citation statements)

References 56 publications

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“…However, these methods lack geometric constraints and scale ambiguity in the learning process. Recently, a combination with geometric constraint depth estimation methods have been proposed [10,11,[32][33][34]. For example, the average depth varies greatly between adjacent frames when there are limited image pixel movement ranges, relative estimated poses, and inconsistent reference frames between the poses [15].…”

Section: Self-supervised Monocular Depth Predictionmentioning

confidence: 99%

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Multi-Sensor Fusion Self-Supervised Deep Odometry and Depth Estimation

et al. 2022

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This paper presents a new deep visual-inertial odometry and depth estimation framework for improving the accuracy of depth estimation and ego-motion from image sequences and inertial measurement unit (IMU) raw data. The proposed framework predicts ego-motion and depth with absolute scale in a self-supervised manner. We first capture dense features and solve the pose by deep visual odometry (DVO), and then combine the pose estimation pipeline with deep inertial odometry (DIO) by the extended Kalman filter (EKF) method to produce the sparse depth and pose with absolute scale. We then join deep visual-inertial odometry (DeepVIO) with depth estimation by using sparse depth and the pose from DeepVIO pipeline to align the scale of the depth prediction with the triangulated point cloud and reduce image reconstruction error. Specifically, we use the strengths of learning-based visual-inertial odometry (VIO) and depth estimation to build an end-to-end self-supervised learning architecture. We evaluated the new framework on the KITTI datasets and compared it to the previous techniques. We show that our approach improves results for ego-motion estimation and achieves comparable results for depth estimation, especially in the detail area.

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Section: Self-supervised Monocular Depth Predictionmentioning

confidence: 99%

“…These methods lead to the dependence on sparse LiDAR data, which are relatively expensive. A recent trend in depth estimation methods involves traditional SLAM [9], which could provide an accurate sparse point cloud, learning to predict monocular depth and odometry in a self-supervised manner [10,11].…”

Section: Introductionmentioning

confidence: 99%