  Depth completion from RGB images and sparse Time-of-Flight (ToF) measurements
is an important problem in computer vision and robotics. While traditional
methods for depth completion have relied on stereo vision or structured light
techniques, recent advances in deep learning have enabled more accurate and
efficient completion of depth maps from RGB images and sparse ToF measurements.
To evaluate the performance of different depth completion methods, we organized
an RGB+sparse ToF depth completion competition. The competition aimed to
encourage research in this area by providing a standardized dataset and
evaluation metrics to compare the accuracy of different approaches. In this
report, we present the results of the competition and analyze the strengths and
weaknesses of the top-performing methods. We also discuss the implications of
our findings for future research in RGB+sparse ToF depth completion. We hope
that this competition and report will help to advance the state-of-the-art in
this important area of research. More details of this challenge and the link to
the dataset can be found at https://mipi-challenge.org/MIPI2023.
