Omnidirectional Depth Estimation for Semantic Segmentation.

This research presents a comprehensive system encompassing semantic segmentation and depth estimation for 360-degree images. It introduces effective methodologies to tackle the challenges associated with depth estimation in panoramic imagery and enhance the precision of semantic segmentation. This article is primarily divided into two sections. The first section emphasizes the significance of integrating depth information in semantic segmentation tasks by comparing its impact to cases where it is not utilized. The second part delves into the discussion of three different approaches to address the spherical distortion in 360-degree images and constructs neural networks for depth estimation. A variety of evaluation metrics are employed to analyze, assess, and compare the results of these three methods while exploring their respective advantages and drawbacks.