Dilated U-Block for Lightweight Indoor Depth Completion With Sobel Edge

Abstract

Depth completion has proven to be the key to obtaining high-precision depth maps from a sparse set of measurements and a single RGB image. However, state-of-the-art depth completion algorithms are based on rather complex deep networks that are unfriendly for multiple applications with limited computational resources and energy. In this paper, we consider the task of lightweight indoor depth completion. We propose an efficient convolution block, called Dilated U-Block (DUB), for multi-level feature extraction and integration. The DUBs and an auxiliary Sobel edge prediction network are used to decrease the number of parameters and model complexity. Comprehensive experiments are performed on the indoor NYU-Depth-v2 dataset. The results show that our proposed approach achieves similar accuracy while requiring only about 5% of parameter size and 20% model complexity compared to the state-of-the-art methods.