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

arXiv: https://arxiv.org/abs/2304.13916 · HTML: https://mlsanity.com/paper/2304.13916/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 25 references that are in the map.
- First posted on arXiv 27 Apr 2023.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [Non-Local Spatial Propagation Network for Depth Completion](https://mlsanity.com/paper/2007.10042/) (2020)
- [PENet: Towards Precise and Efficient Image Guided Depth Completion](https://mlsanity.com/paper/2103.00783/) (2021)
- [Learning Depth with Convolutional Spatial Propagation Network](https://mlsanity.com/paper/1810.02695/) (2018)
- [CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completion](https://mlsanity.com/paper/1911.05377/) (2019)
- [Dynamic Spatial Propagation Network for Depth Completion](https://mlsanity.com/paper/2202.09769/) (2022)
- [Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End](https://mlsanity.com/paper/2006.03349/) (2020)
- [Confidence Propagation through CNNs for Guided Sparse Depth Regression](https://mlsanity.com/paper/1811.01791/) (2018)
- [Depth Coefficients for Depth Completion](https://mlsanity.com/paper/1903.05421/) (2019)
- [Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize](https://mlsanity.com/paper/1707.02937/) (2017)
- [Project to Adapt: Domain Adaptation for Depth Completion from Noisy and Sparse Sensor Data](https://mlsanity.com/paper/2008.01034/) (2020)
- [Depth Completion using Plane-Residual Representation](https://mlsanity.com/paper/2104.07350/) (2021)
- [Estimating Depth from RGB and Sparse Sensing](https://mlsanity.com/paper/1804.02771/) (2018)
- [Depth Completion via Deep Basis Fitting](https://mlsanity.com/paper/1912.10336/) (2019)
- [GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNs](https://mlsanity.com/paper/2210.10758/) (2022)
- [Learning an Efficient Multimodal Depth Completion Model](https://mlsanity.com/paper/2208.10771/) (2022)
- [MIPI 2022 Challenge on RGB+ToF Depth Completion: Dataset and Report](https://mlsanity.com/paper/2209.07057/) (2022)

## Built on by (most cited first, 4 of 4)

- [MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results](https://mlsanity.com/paper/2406.07006/) (2024)
- [MIPI 2024 Challenge on Demosaic for HybridEVS Camera: Methods and Results](https://mlsanity.com/paper/2405.04867/) (2024)
- [SVDC: Consistent Direct Time-of-Flight Video Depth Completion with Frequency Selective Fusion](https://mlsanity.com/paper/2503.01257/) (2025)
- [MIPI 2024 Challenge on Nighttime Flare Removal: Methods and Results](https://mlsanity.com/paper/2404.19534/) (2024)

## Abstract (arXiv)

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.