Massively-Parallel Proximity Queries for Point Clouds [article]

Max Kaluschke, Uwe Zimmermann, Marinus Danzer, Gabriel Zachmann, Rene Weller
2014 Workshop on Virtual Reality Interactions and Physical Simulations  
We present a novel massively-parallel algorithm that allows real-time distance computations between arbitrary 3D objects and unstructured point cloud data. Our main application scenario is collision avoidance for robots in highly dynamic environments that are recorded via a Kinect, but our algorithm can be easily generalized for other applications such as virtual reality. Basically, we represent the 3D object by a bounding volume hierarchy, therefore we adopted the Inner Sphere Trees data
more » ... re Trees data structure, and we process all points of the point cloud in parallel using GPU optimized traversal algorithms. Additionally, all parallel threads share a common upper bound in the minimum distance, this leads to a very high culling efficiency. We implemented our algorithm using CUDA and the results show a real-time performance for online captured point clouds. Our algorithm outperforms previous CPU-based approaches by more than an order of magnitude.
doi:10.2312/vriphys.20141220 dblp:conf/vriphys/KaluschkeZDZW14 fatcat:rlxfstbqhbhf5jd5ey5s7zravu