High-Precision Depth Estimation Networks Using Low-Resolution Depth and RGB Image Sensors for Low-Cost Mixed Reality Glasses

In recent years, with the booming development of the three-dimensional and mixed reality (MR) industries, depth estimation tools have become increasingly important to support many visual problems. Intrinsically, the restriction of computational resources hardly lets complex depth completion methods be implemented on MR glasses. In this paper, we propose a competitive high-precision depth estimation network, which integrates a dual-path autoencoder and adaptive bin depth estimator together. The proposed network with different types of models can aptly fuse a pair of an RGB image and low-resolution low-quality depth map to generate a high-precision depth map. The simplest model of the proposed network has only 2.28 M weights and 1.150 G MACs. It is very lightweight, such that this model could be easily implemented into the platform of low-cost MR glasses to support hand-gesture controls realized in edge devices in real time.