From Virtuality To Reality: A Learning-based Point Cloud Labeling Method With Synthesis Scene
- Resource Type
- Conference
- Authors
- Chen, Runjian; Tang, Li; Ding, Xiaqing; Wang, Yue; Xiong, Rong
- Source
- 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO) Robotics and Biomimetics (ROBIO), 2019 IEEE International Conference on. :1394-1399 Dec, 2019
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
General Topics for Engineers
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Point cloud labeling
Synthesis dataset
PCA
- Language
This paper proposes a machine learning based point cloud labeling algorithm. To classify point cloud in a sparse scan of both virtual and real scene as basic geometrical elements like planar and edge, a rendering dataset in virtual environment is created and labeled. Then the principal component analysis (PCA) is applied to calculate local geometrical features of point cloud. An in-depth analysis is performed by training several machine learning models with PCA features and experiments in which the trained models are applied to on both rendering point cloud and laser scan of real scene are conducted to validate that our approach is scale-invariant and effective on both rendering point cloud and point cloud of real scene.