We introduce a dual, physically meaningful metric for verifying whether a 3D model occupies a hypothesized location in LiDAR scans of a real world scene. We propose two complementary measures: consistency and confidence. The consistency measure uses a free space model along each scanner ray to determine whether the observations are consistent with the hypothesized model location. The confidence measure collects information from the model vertices to determine how much of the model was visible. The metrics do not require training data and are more easily interpretable to a user than typical registration objective function values. We demonstrate the behavior of the dual measures in both synthetic and real world examples.
David Doria and Richard J. Radke, “Consistency and Confidence: A Dual Metric for Verifying 3D Object Detections in Multiple LiDAR Scans”. Proceedings of 3-D Digital Imaging and Modeling (3DIM) 2009, in conjunction with the International Conference on Computer Vision (ICCV), October 2009. (PDF) (Poster)