Invariant training 2D-3D joint hard samples for few-shot point cloud recognition

• Main Authors: YI, Xuanyu, DENG, Jiajun, SUN, Qianru, HUA, Xian-Sheng, LIM, Joo-Hwee, ZHANG, Hanwang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/8389; https://ink.library.smu.edu.sg/context/sis_research/article/9392/viewcontent/Yi_Invariant_Training_2D_3D_Joint_Hard_Samples_for_Few_Shot_Point_Cloud_ICCV_2023_paper__1_.pdf
• Institution: Singapore Management University

We tackle the data scarcity challenge in few-shot point cloud recognition of 3D objects by using a joint prediction from a conventional 3D model and a well-pretrained 2D model. Surprisingly, such an ensemble, though seems trivial, has hardly been shown effective in recent 2D-3D models. We find out t...