Ashish Patel ๐Ÿ‡ฎ๐Ÿ‡ณโ€™s Post

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๐Ÿ”ฅ 6x Linkedln Top Voice | AI Research Scientist & Chief Data Scientist at IBM | Generative AI Expert | Author - Hands-on Time Series Analytics with Python | IBM Quantum ML Certified | 11+ Years in AI | MLOps | IIMA |

๐——๐—ฎ๐˜†-๐Ÿญ๐Ÿฒ๐Ÿฐ Computer Vision Learning ๐—–๐—”๐—ฆ๐—–๐—”๐——๐—˜ ๐—–๐—ข๐—ฆ๐—ง ๐—ฉ๐—ข๐—Ÿ๐—จ๐— ๐—˜ for High-Resolution Multi-View Stereo and Stereo Matching by Alibaba Group A.I. Labs Follow me for similar post:ย ย ๐Ÿ‡ฎ๐Ÿ‡ณ Ashish Patel Interesting Facts : ๐Ÿ”ธ This is a paper in CVPR 2020 with over 52 citations. ๐Ÿ”ธ It Outperforms with the DispNetC,GC-Net,CRL, etc. ------------------------------------------------------------------- ๐—”๐—บ๐—ฎ๐˜‡๐—ถ๐—ป๐—ด ๐—ฅ๐—ฒ๐˜€๐—ฒ๐—ฎ๐—ฟ๐—ฐ๐—ต : https://lnkd.in/eKWUMcz Code : https://lnkd.in/eNDsSqk ------------------------------------------------------------------- ๐—œ๐— ๐—ฃ๐—ข๐—ฅ๐—ง๐—”๐—ก๐—–๐—˜ ๐Ÿ”ธ The deep multi-view stereo (MVS) and stereo matching approaches generally construct 3D cost volumes to regularize and regress the output depth or disparity.These methods are limited when high-resolution outputs are needed since the memory and time costs grow cubically as the volume resolution increases. ๐Ÿ”ธ Propose a both memory and time efficient cost volume formulation that is complementary to existing multi-view stereo and stereo matching approaches based on 3D cost volumes. First, the proposed cost volume is built upon a standard feature pyramid encoding geometry and context at gradually finer scales. #computervision #artificialintelligence #technology

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Ashish Patel ๐Ÿ‡ฎ๐Ÿ‡ณ

๐Ÿ”ฅ 6x Linkedln Top Voice | AI Research Scientist & Chief Data Scientist at IBM | Generative AI Expert | Author - Hands-on Time Series Analytics with Python | IBM Quantum ML Certified | 11+ Years in AI | MLOps | IIMA |

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Ashish Patel ๐Ÿ‡ฎ๐Ÿ‡ณ

๐Ÿ”ฅ 6x Linkedln Top Voice | AI Research Scientist & Chief Data Scientist at IBM | Generative AI Expert | Author - Hands-on Time Series Analytics with Python | IBM Quantum ML Certified | 11+ Years in AI | MLOps | IIMA |

2y

๐Ÿ”ธThen, Narrow the depth (or disparity) range of each stage by the depth (or disparity) map from the previous stage. With gradually higher cost volume resolution and adaptive adjustment of depth (or disparity) intervals, the output is recovered in a coarser to fine manner.

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