mlsanityData to 14 Oct 2026

Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report

arXiv 2105.08630 ↗First posted 17 May 2021eess.IV cs.CV cs.LGcited by 19

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Abstract (arXiv)

Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms. For this, the participants were provided with a new large-scale dataset containing RGB-depth image pairs obtained with a dedicated stereo ZED camera producing high-resolution depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the popular Raspberry Pi 4 platform with a mobile ARM-based Broadcom chipset. The proposed solutions can generate VGA resolution depth maps at up to 10 FPS on the Raspberry Pi 4 while achieving high fidelity results, and are compatible with any Android or Linux-based mobile devices. A detailed description of all models developed in the challenge is provided in this paper.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 51 of its 71 references that are in the map. First posted on arXiv 17 May 2021.

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51 of 71 references.

References · 51

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  5. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  6. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  7. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  8. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,546
  9. 2017Non-local Neural Networkscited by 2,293
  10. 2019Searching for MobileNetV3cited by 1,994
  11. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,593
  12. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,495
38 more
  1. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  2. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,274
  3. 2018MnasNet: Platform-Aware Neural Architecture Search for Mobilecited by 943
  4. 2018MegaDepth: Learning Single-View Depth Prediction from Internet Photoscited by 737
  5. 2020AdaBins: Depth Estimation using Adaptive Binscited by 601
  6. 2018FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Searchcited by 485
  7. 2016Deeper Depth Prediction with Fully Convolutional Residual Networkscited by 403
  8. 2019From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimationcited by 382
  9. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  10. 2020ZeroQ: A Novel Zero Shot Quantization Frameworkcited by 269
  11. 2018Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithmcited by 235
  12. 2019MetaPruning: Meta Learning for Automatic Neural Network Channel Pruningcited by 227
  13. 2016Single-Image Depth Perception in the Wildcited by 180
  14. 2015Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fieldscited by 169
  15. 2017DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networkscited by 156
  16. 2019Quantization Networkscited by 153
  17. 2020FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensionscited by 152
  18. 2018High Quality Monocular Depth Estimation via Transfer Learningcited by 133
  19. 2020Replacing Mobile Camera ISP with a Single Deep Learning Modelcited by 108
  20. 2014Deep Convolutional Neural Fields for Depth Estimation from a Single Imagecited by 106
  21. 2019FastDepth: Fast Monocular Depth Estimation on Embedded Systemscited by 94
  22. 2019Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural Networkscited by 90
  23. 2020NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Resultscited by 83
  24. 2019AI Benchmark: All About Deep Learning on Smartphones in 2019cited by 77
  25. 2018AI Benchmark: Running Deep Neural Networks on Android Smartphonescited by 75
  26. 2019Learning Filter Basis for Convolutional Neural Network Compressioncited by 57
  27. 2020Rendering Natural Camera Bokeh Effect with Deep Learningcited by 57
  28. 2020NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Resultscited by 51
  29. 2020AIM 2020 Challenge on Learned Image Signal Processing Pipelinecited by 44
  30. 2018PIRM Challenge on Perceptual Image Enhancement on Smartphones: Reportcited by 38
  31. 2017WESPE: Weakly Supervised Photo Enhancer for Digital Camerascited by 32
  32. 2019On-Device Neural Net Inference with Mobile GPUscited by 32
  33. 2018Towards real-time unsupervised monocular depth estimation on CPUcited by 26
  34. 2020AIM 2020 Challenge on Rendering Realistic Bokehcited by 26
  35. 2020T-Basis: a Compact Representation for Neural Networkscited by 21
  36. 2020Real-time single image depth perception in the wild with handheld devicescited by 19
  37. 2020Deploying Image Deblurring across Mobile Devices: A Perspective of Quality and Latencycited by 16
  38. 2020Controlling Information Capacity of Binary Neural Networkcited by 15

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Cited by year

Citing papers per year: 2021: 3; 2022: 9; 2023: 3; 2024: 2; 2025: 2.

19 papers.

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  1. 2022Model-Based Image Signal Processors via Learnable Dictionariescited by 43
  2. 2022Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Reportcited by 16
  3. 2022Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Reportcited by 13
  4. 2023The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimationcited by 13
  5. 2023The Second Monocular Depth Estimation Challengecited by 12
  6. 2025TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusioncited by 9
  7. 2023Diffusion-Augmented Depth Prediction with Sparse Annotationscited by 8
  8. 2021Lightweight Monocular Depth with a Novel Neural Architecture Search Methodcited by 7
  9. 2022Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Reportcited by 4
  10. 2022Hybrid Skip: A Biologically Inspired Skip Connection for the UNet Architecturecited by 3
  11. 2022Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Reportcited by 3
  12. 2025NTIRE 2025 Challenge on HR Depth from Images of Specular and Transparent Surfacescited by 3
7 more
  1. 2022Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Reportcited by 2
  2. 2021Monocular Depth Estimation Primed by Salient Point Detection and Normalized Hessian Losscited by 1
  3. 2022LiteDepth: Digging into Fast and Accurate Depth Estimation on Mobile Devicescited by 1
  4. 2024Virtually Enriched NYU Depth V2 Dataset for Monocular Depth Estimation: Do We Need Artificial Augmentation?cited by 1
  5. 2024Configurable Holography: Towards Display and Scene Adaptationcited by 1
  6. 2021SmartDepthSync: Open Source Synchronized Video Recording System of Smartphone RGB and Depth Camera Range Image Frames with Sub-millisecond Precisioncited by 0
  7. 2022Fast Neural Architecture Search for Lightweight Dense Prediction Networkscited by 0