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

@article{Ignatov2021FastAA,
  title={Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report},
  author={Andrey D. Ignatov and Grigory Malivenko and D. Plowman and Samarth Shukla and Radu Timofte and Ziyu Zhang and Yicheng Wang and Zilong Huang and Guozhong Luo and Gang Yu and Bin Fu and Yiran Wang and Xingyi Li and Minghan Shi and Ke Xian and Zhiguo Cao and Jin-Hua Du and Pei Wu and Chao Ge and Jiaoyang Yao and Fangwen Tu and Bo Li and Jung Eun Yoo and Kwanggyoon Seo and Jialei Xu and Zhenyu Li and Xianming Liu and Junjun Jiang and Wei-Chih Chen and Shayan Joya and Huanhuan Fan and Zhaobing Kang and Ang Li and Tianpeng Feng and Yang Liu and Chuannan Sheng and Jian Yin and Fausto T. Benavide},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  year={2021},
  pages={2545-2557}
}
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… Expand
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Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report
TLDR
The first Mobile AI challenge is introduced, where the target is to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms. Expand
Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report
  • Andrey D. Ignatov, Kim Byeoung-su, +29 authors Feifei Chen
  • Computer Science, Engineering
  • 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  • 2021
TLDR
The first Mobile AI challenge is introduced, where the target is to develop an end-to-end deep learning-based image denoising solution that can demonstrate high efficiency on smartphone GPUs. Expand
Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report
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The target is to develop an end-to-end deep learning-based video super-resolution solutions that can achieve a realtime performance on mobile GPUs and can upscale videos to HD resolution at up to 80 FPS while demonstrating high fidelity results. Expand
Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report
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  • 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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TLDR
The first Mobile AI challenge is introduced, where the target is to develop an end-to-end deep learning-based image super-resolution solutions that can demonstrate a real-time performance on mobile or edge NPUs. Expand
Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report
TLDR
The target was to develop an end-to-end deep learning-based image signal processing (ISP) pipeline that can replace classical hand-crafted ISPs and achieve nearly real-time performance on smartphone NPUs. Expand
A Simple Baseline for Fast and Accurate Depth Estimation on Mobile Devices
In this paper, we propose a simple but effective encoder-decoder based network for fast and accurate depth estimation on mobile devices. Unlike other depth estimation methods using heavy contextExpand
FastDepth: Fast Monocular Depth Estimation on Embedded Systems
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Real-Time Single Image Depth Perception in the Wild with Handheld Devices
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Knowledge Distillation for Fast and Accurate Monocular Depth Estimation on Mobile Devices
Fast and accurate monocular depth estimation on mobile devices is a challenging task as one should always trade off the accuracy against the inference time. Most monocular depth methods adopt modelsExpand
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