Paper ID | 3D-2.10 | ||
Paper Title | A METRIC FOR EVALUATING 3D RECONSTRUCTION AND MAPPING PERFORMANCE WITH NO GROUND TRUTHING | ||
Authors | Guoxiang Zhang, YangQuan Chen, University of California, Merced, United States | ||
Session | 3D-2: Point Cloud Processing 2 | ||
Location | Area J | ||
Session Time: | Wednesday, 22 September, 08:00 - 09:30 | ||
Presentation Time: | Wednesday, 22 September, 08:00 - 09:30 | ||
Presentation | Poster | ||
Topic | Three-Dimensional Image and Video Processing: Point cloud processing | ||
IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
Abstract | It is not easy when evaluating 3D mapping performance because existing metrics require ground truth data that can only be collected with special instruments. In this paper, we propose a metric, dense map posterior (DMP), for this evaluation. It can work without any ground truth data. Instead, it calculates a comparable value, reflecting a map posterior probability, from dense point cloud observations. In our experiments, the proposed DMP is benchmarked against ground truth-based metrics. Results show that DMP can provide a similar evaluation capability. The proposed metric makes evaluating different methods more flexible and opens many new possibilities, such as self-supervised methods and more available datasets. |