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Document type:
Konferenzbeitrag 
Author(s):
Lin, Jianjie; Rickert, Markus; Knol, Aloisl 
Title:
Deep Hierarchical Rotation Invariance Learning with Exact Geometry Feature Representation for Point Cloud Classification 
Abstract:
Rotation invariance is a crucial property for 3D object classification, which is still a challenging task. State-of- the-art deep learning-based works require a massive amount of data augmentation to tackle this problem. This is however inefficient and classification accuracy suffers a sharp drop in experiments with arbitrary rotations. We introduce a new descriptor that can globally and locally capture the surface geometry properties and is based on a combination of spher- ical harmonic...    »
 
Book / Congress title:
Proceedings of the International Conference on Robotics and Automation (ICRA) 
Year:
2021