MIPI 2023 Challenge on RGB+ToF Depth Completion: Methods and Results

• Published in: arXiv.org
• Main Authors: Zhu, Qingpeng, Sun, Wenxiu, Dai, Yuekun, Li, Chongyi, Zhou, Shangchen, Feng, Ruicheng, Sun, Qianhui, Chen Change Loy, Gu, Jinwei, Yu, Yi, Huang, Yangke, Zhang, Kang, Chen, Meiya, Wang, Yu, Li, Yongchao, Jiang, Hao, Muduli, Amrit Kumar, Kumar, Vikash, Swami, Kunal, Bajpai, Pankaj Kumar, Ma, Yunchao, Xiao, Jiajun, Ling, Zhi
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 27.04.2023
• Subjects: Color imagery; Competition; Computer vision; Datasets; Performance evaluation; Robotics

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.