## 1 Introduction  
Depth estimation is important in computer vision for robot control [1], multi-view generation for 3D exhibition [2, 3, 4], autonomous driving [5], hand-gesture recognition in mixed reality (MR) [6] and so forth. In general, depth estimations can be roughly catalogued into image-based and sensor-based approaches. Image-based approaches include stereo matching [7] and monocular depth estimation [8, 9, 10, 11, 12, 13, 14, 15, 16], which purely exploit the RGB image resource to achieve the depth estimation task. Monocular depth estimation networks can be cataloged into supervised [8], unsupervised [13] and semi-supervised [14, 15, 16] approaches. The training resource of supervised monocular depth estimation networks could primarily come from infrared, LiDAR, radar and ultrasonic sensors [17]. Thus, the primary depths are manually corrected according to the accurate depth distances to obtain the ground truth (GT) depth maps. However, in most cases, the labeling and annotation of GT depths cannot be easily available. Thus, unsupervised networks adopt paired stereo color images captured by dual cameras to indirectly infer the GT depth map for each other. The merit of developing an unsupervised network is that the depth sensor rendering the GT depth map is not needed at all. The depth map predicted from a color view is applied to render the virtual RGB image of another view using depth-image-based rendering (DIBR) [3]. Thus, the prediction of depth loss can be transferred to the RGB domain so that the training loss is only associated with the varied RGB image differences. Semi-supervised networks are conceptually similar to the unsupervised ones in that they can be divided into two classes. The first class utilizes dual-view RGB images combined with semantic segmentation information and 3D-reconstructed surface normal information to achieve multi-functional networks [15]. The embedding of normal segmentation and depth can be informatively harmonized to enhance the accuracy of depth estimation. The second class integrates dual-view RGB images and low-resolution depth maps [15] so that their loss functions are similar to those of unsupervised approaches, where calibrations are partially required in supervised processing. Users would not like to adopt parallelized RGB cameras for obtaining the parallax in depth estimation when the persistence of on-line calibration is troublesome for the use of moving stereo cameras with inevitable vibrations. Thus, the monocular depth estimation network appears common in use for cost-effective depth estimation. Further, monocular depth estimation with the aid of a LiDAR sensor [17] or time-of-flight (ToF) sensor [18] is preferred for predicting real-world distances. The ToF sensors of LED light sources are cheap but supply low accuracies and a shorter range of sensed depths relative to LiDAR sensors. MR glasses in mobile features are often with computation-constrained resources, making it difficult for complex state-of-the-art depth methods to be designed in them. Hence, the low power-consumption ToF depth sensor is the first choice for installation in smart MR glasses. Although the low-cost ToF depth sensor can only offer spare depths, in principle, the high effective compensation of spare depth maps and RGB images shall capably figure out a high-precision depth map with partial practical distances. To obtain precise high dimension depths for MR glasses, in this paper, we design a bin-based depth estimation network with variant decoders. It can create high-precision depth from an RGB image with a low-quality sparse depth map. By an appropriated bin-based network, the simplest model of the proposed network can be very lightweight. The contributions of the proposed depth estimation networks are threefold.