Robust and Flexible Omnidirectional Depth Estimation with Multiple 360-degree Cameras

TL;DR

This paper introduces robust and flexible omnidirectional depth estimation methods using multiple 360-degree cameras, leveraging geometric constraints and redundant information to improve accuracy and resilience in real-world conditions.

Contribution

The paper presents two novel algorithms for multi-view omnidirectional depth estimation and introduces a synthetic dataset that accounts for camera soiling and glare.

Findings

Achieved state-of-the-art depth estimation accuracy.

Demonstrated robustness to camera soiling and environmental variations.

Validated flexibility across different camera configurations.

Abstract

Omnidirectional depth estimation has received much attention from researchers in recent years. However, challenges arise due to camera soiling and variations in camera layouts, affecting the robustness and flexibility of the algorithm. In this paper, we use the geometric constraints and redundant information of multiple 360-degree cameras to achieve robust and flexible multi-view omnidirectional depth estimation. We implement two algorithms, in which the two-stage algorithm obtains initial depth maps by pairwise stereo matching of multiple cameras and fuses the multiple depth maps to achieve the final depth estimation; the one-stage algorithm adopts spherical sweeping based on hypothetical depths to construct a uniform spherical matching cost of the multi-camera images and obtain the depth. Additionally, a generalized epipolar equirectangular projection is introduced to simplify the spherical epipolar constraints. To overcome panorama distortion, a spherical feature extractor is implemented. Furthermore, a synthetic 360-degree dataset consisting of 12K road scene panoramas and 3K ground truth depth maps is presented to train and evaluate 360-degree depth estimation algorithms. Our dataset takes soiled camera lenses and glare into consideration, which is more consistent with the real-world environment. Experiments show that our two algorithms achieve state-of-the-art performance, accurately predicting depth maps even when provided with soiled panorama inputs. The flexibility of the algorithms is experimentally validated in terms of camera layouts and numbers.