3DGeoInfo 2023 talk on efficient point cloud query

Hao Li was presenting results of our efficient point cloud query paper (Teuscher et al., 2024) led by Balthasar Teuscher at the 18th 3DGeoInfo 2023 conference in Munich.

In this paper, we propose an efficient in-memory point cloud processing solution and implementation demonstrating that the inherent technical identity of the memory location of a point (e.g., a memory pointer) is both sufficient and elegant to avoid gridding as long as the point cloud fits into the main memory of the computing system. During the conference, we have collected a handful of nice comments and suggestions for the participants, which will be integrated in the future development. This paper is a nice joint effort with TUM colleagues from Chair of Engineering Geodesy (Prof. Holst) and Professorship for Remote Sensing Applications (Prof. Anders).

Resources

• Github Repository

1. Teuscher, B., Geißendörfer, O., Luo, X., Li, H., Anders, K., Holst, C., & Werner, M. (2024). Efficient In-Memory Point Cloud Query Processing. In T. H. Kolbe, A. Donaubauer, & C. Beil (Eds.), Recent Advances in 3D Geoinformation Science (pp. 267–286). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43699-4_16 [PDF] [Online]