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X3SEG: Model-Agnostic Explanations for the Semantic Segmentation of 3D Point Clouds with Prototypes and Criticism

: Heide, Nina Felicitas; Müller, Erik; Petereit, Janko; Heizmann, Michael


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society:
IEEE International Conference on Image Processing, ICIP 2021 : 19-22 September 2021, Anchorage, Alaska, USA
Piscataway, NJ: IEEE, 2021
ISBN: 978-1-6654-4115-5
ISBN: 978-1-6654-3102-6
International Conference on Image Processing (ICIP) <2021, Anchorage/Alas.>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Robotic Systems for Decontamination in Hazardous Environments
Fraunhofer IOSB ()
explainable artificial intelligence; Semantic Segmentation; autonomous system

The proposed X3Seg approach generates model-agnostic, example-based explanations for the semantic segmentation of 3D point clouds. It retrieves the most similar 3D point sets (prototypes) as well as the most dissimilar point sets (criticism) to the spatially connected 3D point set which is to be explained. X3Seg comprises three methods for a holistic understanding of point-by-point class predictions: encompassing, selective, and predictive X3Seg. Prototypes and criticism are identified from a particularly generated prototype database by combining different similarity measures. To the best of our knowledge, X3Seg is the first model-agnostic explainable artificial intelligence (XAI) approach providing example-based explanations for the semantic segmentation of 3D data with prototypes and criticism. It is demonstrated on RangeNet53++[1] predictions for 3D point cloud data from the SemanticKITTI dataset.