A Study of Conventional and Learning-Based Depth Estimators for Immersive Video Transmission

Abstract

Obtaining an accurate depth map of a scene is very important for major applications like immersive video, robotics, autonomous driving, and many more. The different methods to estimate depths can be classified as conventional and learning-based methods. While these methods have been studied for their depth accuracy, less attention has been paid to studying their performance in the use case of depth image-based rendering (DIBR). Here we study and evaluate two conventional methods and five learning-based methods for a real-world use case of immersive video transmission in the context of MPEG-I. The user-requested views are synthesized using Test Model for Immersive Video (TMIV) from the depth maps obtained by all methods and original texture views. The synthesized images are compared with their original counterparts using various quality metrics.