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From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection
[article]
2021
arXiv
pre-print
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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
2021
IEEE transactions on circuits and systems for video technology (Print)
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
[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