Panoramic depth estimation via supervised and unsupervised learning in indoor scenes
Keyang Zhou, Kailun Yang, Kaiwei Wang
Abstract
Depth estimation, as a necessary clue to convert 2D images into the 3D space, has been applied in many machine vision areas. However, to achieve an entire surrounding 360° geometric sensing, traditional stereo matching algorithms for depth estimation are limited due to large noise, low accuracy, and strict requirements for multi-camera calibration. In this work, for a unified surrounding perception, we introduce panoramic images to obtain a larger field of view. We extend PADENet [IEEE 23rd International Conference on Intelligent Transportation Systems, (2020), pp. 1-610.1109/ITSC45102.2020.9294206], which first appeared in our previous conference work for outdoor scene understanding, to perform panoramic monocular depth estimation with a focus for indoor scenes. At the same time, we improve the training process of the neural network adapted to the characteristics of panoramic images. In addition, we fuse the traditional stereo matching algorithm with deep learning methods and further improve the accuracy of depth predictions. With a comprehensive variety of experiments, this research demonstrates the effectiveness of our schemes aiming for indoor scene perception.