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Paper Detail

Paper ID3D-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
Session3D-2: Point Cloud Processing 2
LocationArea 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.