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From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection [article]

Jiajun Deng, Wengang Zhou, Yanyong Zhang, Houqiang Li
2021 arXiv   pre-print
To this end, in this work, we regard point clouds as hollow-3D data and propose a new architecture, namely Hallucinated Hollow-3D R-CNN (H^23D R-CNN), to address the problem of 3D object detection.  ...  However, point clouds are always sparsely distributed in the 3D space, and with unstructured storage, which makes it difficult to represent them for effective 3D object detection.  ...  CONCLUSION In this work, we are dedicated to 3D object detection and propose a new Hallucinated Hollow-3D R-CNN framework.  ... 
arXiv:2107.14391v1 fatcat:ucy3rhray5dznntzb6y47ierxu

Guest Editorial Introduction to the Special Issue on Recent Advances in Point Cloud Processing and Compression

Zhu Li, Shan Liu, Frederic Dufaux, Li Li, Ge Li, C.-C. Jay Kuo
2021 IEEE transactions on circuits and systems for video technology (Print)  
[A13] deem point clouds as the hollow-3D data and present a new object detection architecture hallucinated hollow-3D R-CNN (H23D R-CNN).  ...  Comprehensive experiments demonstrate the effectiveness of the T3D framework for 3D object detection. VI.  ... 
doi:10.1109/tcsvt.2021.3129071 fatcat:hw6chtchp5dzbacriqj7k4tz7q