Parallax Inference for Robust Temporal Monocular Depth Estimation in Unstructured Environments

Abstract

Estimating the distance to objects is crucial for autonomous vehicles, but cost, weight or power constraints sometimes prevent the use of dedicated depth sensors. In this case, the distance has to be estimated from on-board mounted RGB cameras, which is a complex task especially for environments such as natural outdoor landscapes. In this paper, we present a new depth estimation method suitable for use in such landscapes. First, we establish a bijective relationship between depth and the visual parallax of two consecutive frames and show how to exploit it to perform motion-invariant pixel-wise depth estimation. Then, we detail our architecture which is based on a pyramidal convolutional neural network where each level refines an input parallax map estimate by using two customized cost volumes. We use these cost volumes to leverage the visual spatio-temporal constraints imposed by motion and make the network robust for varied scenes. We benchmarked our approach both in test and generalization modes on public datasets featuring synthetic camera trajectories recorded in a wide variety of outdoor scenes. Results show that our network outperforms the state of the art on these datasets, while also performing well on a standard depth estimation benchmark.

Lightweight 2D Map Construction of Vehicle Environments Using a Semi-Supervised Depth Estimation Approach

“…The authors of paper [5] used video sequences and time recurrence in tandem with geometric constraints to estimate depth maps. They introduced a spatial reprojection layer to maintain the spatio-temporal consistency between the levels.…”

A Distance Measurement Approach for Large Fruit Picking with Single Camera

“…With the location information of the target fruit, the picking robot can perform the supposed work [5,6]. As one of the machine vision methods for target distance assessment, monocular distance measurement has been applied in all sorts of fields, such as industrial robots [7], medical treatment [8], vehicle control [9], and fruit picking [10,11]. Compared to binocular vision and laser vision, it was more convenient and simpler and had great further development potential [12,13].…”