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

Ignatov, Andrey; Malivenko, Grigory; Plowman, David; Shukla, Samarth; Timofte, Radu; Zhang, Ziyu; Wang, Yicheng; Huang, Zilong; Luo, Guozhong; Yu, Gang; Fu, Bin; Wang, Yiran; Li, Xingyi; Shi, Min; Xian, Ke; Cao, Zhiguo; Du, Jinhua; Wu, Pei‐Lin; Ge, Chao; Yao, Jiaoyang; Tu, Fangwen; Li, Bo; Yoo, Jung Eun; Seo, Kwanggyoon; Xu, Jialei; Li, Zhenyu; Liu, Xianming; Jiang, Junjun; Chen, Wei-Chi; Joya, Shayan; Fan, Huanhuan; Kang, Zhaobing; Li, Ang; Feng, Tao; Liu, Yang; Sheng, Chuannan; Yin, Jian; Benavides, Fausto T.

doi:10.1109/cvprw53098.2021.00288

Cited by 36 publications

(21 citation statements)

References 35 publications

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“…• Learned Smartphone ISP on Mobile NPUs [18] • Real Image Denoising on Mobile GPUs [17] • Quantized Image Super-Resolution on Edge SoC NPUs • Real-Time Video Super-Resolution on Mobile GPUs [25] • Single-Image Depth Estimation on Mobile Devices [21] • Quantized Camera Scene Detection on Smartphones [22] • High Dynamic Range Image Processing on Mobile NPUs…”

Section: Introductionmentioning

confidence: 99%

Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report

Ignatov¹,

Timofte²,

Denna³

et al. 2021

Preprint

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Image super-resolution is one of the most popular computer vision problems with many important applications to mobile devices. While many solutions have been proposed for this task, they are usually not optimized even for common smartphone AI hardware, not to mention more constrained smart TV platforms that are often supporting INT8 inference only. To address this problem, we introduce the first Mobile AI challenge, 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. For this, the participants were provided with the DIV2K dataset and trained quantized models to do an efficient 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated NPU capable of accelerating quantized neural networks. The proposed solutions are fully compatible with all major mobile AI accelerators and are capable of reconstructing Full HD images under 40-60 ms while achieving high fidelity results. A detailed description of all models developed in the challenge is provided in this paper.

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Section: Introductionmentioning

confidence: 99%

Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report

Ignatov¹,

Timofte²,

Denna³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…• Learned Smartphone ISP on Mobile NPUs • Real Image Denoising on Mobile GPUs [15] • Quantized Image Super-Resolution on Mobile NPUs [24] • Real-Time Video Super-Resolution on Mobile GPUs [43] • Single-Image Depth Estimation on Mobile Devices [18] • Quantized Camera Scene Detection on Smartphones [19] • High Dynamic Range Image Processing on Mobile NPUs…”

Section: Introductionmentioning

confidence: 99%

Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report

Ignatov¹,

Chiang²,

Kuo³

et al. 2021

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As the quality of mobile cameras starts to play a crucial role in modern smartphones, more and more attention is now being paid to ISP algorithms used to improve various perceptual aspects of mobile photos. In this Mobile AI challenge, 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. For this, the participants were provided with a novel learned ISP dataset consisting of RAW-RGB image pairs captured with the Sony IMX586 Quad Bayer mobile sensor and a professional 102megapixel medium format camera. The runtime of all models was evaluated on the MediaTek Dimensity 1000+ platform with a dedicated AI processing unit capable of accelerating both floating-point and quantized neural networks. The proposed solutions are fully compatible with the above NPU and are capable of processing Full HD photos under 60-100 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.

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“…• Learned Smartphone ISP on Mobile NPUs [11] • Real Image Denoising on Mobile GPUs [10] • Quantized Image Super-Resolution on Edge SoC NPUs [19] • Real-Time Video Super-Resolution on Mobile GPUs [16] • Single-Image Depth Estimation on Mobile Devices [13] • Quantized Camera Scene Detection on Smartphones…”

Section: Introductionmentioning

confidence: 99%

Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report

Ignatov¹,

Malivenko²,

Timofte³

et al. 2021

Preprint

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Camera scene detection is among the most popular computer vision problem on smartphones. While many custom solutions were developed for this task by phone vendors, none of the designed models were available publicly up until now. To address this problem, we introduce the first Mobile AI challenge, 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. For this, the participants were provided with a large-scale CamSDD dataset consisting of more than 11K images belonging to the 30 most important scene categories. The runtime of all models was evaluated on the popular Apple Bionic A11 platform that can be found in many iOS devices. The proposed solutions are fully compatible with all major mobile AI accelerators and can demonstrate more than 100-200 FPS on the majority of recent smartphone platforms while achieving a top-3 accuracy of more than 98%. A detailed description of all models developed in the challenge is provided in this paper.

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

Cited by 36 publications

References 35 publications

Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report

Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report

Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report

Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report

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