๐ฅ 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 |
๐๐ฎ๐-๐ญ๐ฒ๐ฐ Computer Vision Learning ๐๐๐ฆ๐๐๐๐ ๐๐ข๐ฆ๐ง ๐ฉ๐ข๐๐จ๐ ๐ for High-Resolution Multi-View Stereo and Stereo Matching by Alibaba Group A.I. Labs Follow me for similar post:ย ย ๐ฎ๐ณ Ashish Patel Interesting Facts : ๐ธ This is a paper in CVPR 2020 with over 52 citations. ๐ธ It Outperforms with the DispNetC,GC-Net,CRL, etc. ------------------------------------------------------------------- ๐๐บ๐ฎ๐๐ถ๐ป๐ด ๐ฅ๐ฒ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต : https://lnkd.in/eKWUMcz Code : https://lnkd.in/eNDsSqk ------------------------------------------------------------------- ๐๐ ๐ฃ๐ข๐ฅ๐ง๐๐ก๐๐ ๐ธ 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. ๐ธ 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
๐ธThen, 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.
๐ฅ 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 |
2yFor previous post follow this github : https://github.com/ashishpatel26/365-Days-Computer-Vision-Learning-Linkedin-Post