TR2019-039

Large-Scale 3D Point Cloud Representations via Graph Inception Networks with Applications to Autonomous Driving


    •  Chen, S., "Large-Scale 3D Point Cloud Representations via Graph Inception Networks with Applications to Autonomous Driving", Graph Signal Processing Workshop (GSP), DOI: 10.1109/ICIP.2019.8803525, June 2019, pp. 4395-4399.
      BibTeX TR2019-039 PDF
      • @inproceedings{Chen2019jun,
      • author = {Chen, Siheng},
      • title = {Large-Scale 3D Point Cloud Representations via Graph Inception Networks with Applications to Autonomous Driving},
      • booktitle = {Graph Signal Processing Workshop (GSP)},
      • year = 2019,
      • pages = {4395--4399},
      • month = jun,
      • doi = {10.1109/ICIP.2019.8803525},
      • url = {https://www.merl.com/publications/TR2019-039}
      • }
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  • Research Areas:

    Artificial Intelligence, Computer Vision, Machine Learning

With the growth of 3D sensing technologies, one can now use a large number of 3D points to precisely represent objects’ surfaces and surrounding environments. We call those 3D points a 3D point cloud; it has a growing impact on various applications, including autonomous driving, drones, robotics, virtual reality and preservation of historical artifacts [4]. For example, a self-driving car could use multiple sensors to observe the world, such as LiDARs, cameras and RADARs [2]. Among those, LiDARs produce two types of 3D point clouds: real-time LiDAR sweeps and high-definition maps. Both types of 3D point clouds provides accurate range information for self-driving cars, which are critical to localization and perception systems. We consider these point clouds large-scale point clouds because they contain a large number of 3D points and record outdoor, open areas.