Deep metric learning on point sets for 3D industry elements recognition

Kuan Xu; Xudong Li; Hongzhi Jiang; Huijie Zhao

doi:10.1117/12.2505579

12 December 2018 Deep metric learning on point sets for 3D industry elements recognition

Kuan Xu, Xudong Li, Hongzhi Jiang, Huijie Zhao

Proceedings Volume 10846, Optical Sensing and Imaging Technologies and Applications; 108462Q (2018) https://doi.org/10.1117/12.2505579
Event: International Symposium on Optoelectronic Technology and Application 2018, 2018, Beijing, China

Abstract

Point cloud is an important data type for representing the geometric characteristics of industry elements. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images and apply existing mature deep learning framework on it. In this paper, we proposed a deep metric learning based network which projects point sets into embedding space to pull the intra-class samples closer and push the inter-class samples far away. To further facilitate the future research on this problem, a new dataset (Industry Element 8) containing 8 industry elements cloud point is built. Experimental results have demonstrated the superior performance of our proposed learning network.

Citation Download Citation

Kuan Xu, Xudong Li, Hongzhi Jiang, and Huijie Zhao "Deep metric learning on point sets for 3D industry elements recognition", Proc. SPIE 10846, Optical Sensing and Imaging Technologies and Applications, 108462Q (12 December 2018); https://doi.org/10.1117/12.2505579

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