2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDSS-EDNN.1
Paper Title X3Seg: Model-agnostic Explanations for the Semantic Segmentation of 3D Point Clouds with Prototypes and Criticism
Authors Nina Felicitas Heide, Erik Müller, Janko Petereit, Fraunhofer IOSB, Germany; Michael Heizmann, Karlsruhe Institute of Technology, Germany
SessionSS-EDNN: Special Session: Explainable Deep Neural Networks for Image/Video Processing
LocationArea B
Session Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Poster
Topic Special Sessions: Explainable Deep Neural Networks for Image/Video Processing
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Abstract 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 [2].