𝗗𝗮𝘆-𝟭𝟯𝟯 Computer Vision Learning 𝗣𝗼𝗶𝗻𝘁𝗚𝗿𝗼𝘂𝗽: Dual-Set Point Grouping for 3D Instance Segmentation by The Chinese University of Hong Kong Follow me for similar post : @🇮🇳 Ashish Patel Interesting Facts : 🔸 This is a paper in CVPR 2020 with over 30 citations. 🔸 It Outperforms with SPGN, 3D-BEVIS, R-PointNet, DPC, 3D-SIS, MASC, PanopticFusion, 3D-BoNet, MTML etc. ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://lnkd.in/eFzMqV2 Code : https://lnkd.in/eUdMgk4 ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 PointGroup, a new end-to-end bottom-up architecture, specifically focused on better grouping the points by exploring the void space between objects. 🔸 Design of a two-branch network to extract point features and predict semantic labels and offsets, for shifting each point towards its respective instance centroid. A clustering component is followed to utilize both the original and offset-shifted point coordinate sets, taking advantage of their complementary strength. #computervision #artificialintelligence #deeplearning
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4y🔸 the ScoreNet to evaluate the candidate instances, followed by the Non-Maximum Suppression (NMS) to remove duplicates. For previous post visit this github : https://github.com/ashishpatel26/365-Days-Computer-Vision-Learning-Linkedin-Post