  Developing and integrating advanced image sensors with novel algorithms in
camera systems is prevalent with the increasing demand for computational
photography and imaging on mobile platforms. However, the lack of high-quality
data for research and the rare opportunity for in-depth exchange of views from
industry and academia constrain the development of mobile intelligent
photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI
challenge including five tracks focusing on novel image sensors and imaging
algorithms. In this paper, RGB+ToF Depth Completion, one of the five tracks,
working on the fusion of RGB sensor and ToF sensor (with spot illumination) is
introduced. The participants were provided with a new dataset called
TetrasRGBD, which contains 18k pairs of high-quality synthetic RGB+Depth
training data and 2.3k pairs of testing data from mixed sources. All the data
are collected in an indoor scenario. We require that the running time of all
methods should be real-time on desktop GPUs. The final results are evaluated
using objective metrics and Mean Opinion Score (MOS) subjectively. A detailed
description of all models developed in this challenge is provided in this
paper. More details of this challenge and the link to the dataset can be found
at https://github.com/mipi-challenge/MIPI2022.
