False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation

• URL: http://arxiv.org/abs/2110.15681v1
• Date: Fri, 29 Oct 2021 11:00:30 GMT
• Title: False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation
• Authors: Pascal Colling, Matthias Rottmann, Lutz Roese-Koerner, Hanno Gottschalk
• Abstract summary: We present a novel post-processing tool for semantic segmentation of LiDAR point cloud data, called LidarMetaSeg. We compute dispersion measures based on network probability outputs as well as feature measures based on point cloud input features and aggregate them on segment level. These aggregated measures are used to train a meta classification model to predict whether a predicted segment is a false positive or not and a meta regression model to predict the segmentwise intersection over union.
• Score: 5.735035463793009
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel post-processing tool for semantic segmentation of LiDAR point cloud data, called LidarMetaSeg, which estimates the prediction quality segmentwise. For this purpose we compute dispersion measures based on network probability outputs as well as feature measures based on point cloud input features and aggregate them on segment level. These aggregated measures are used to train a meta classification model to predict whether a predicted segment is a false positive or not and a meta regression model to predict the segmentwise intersection over union. Both models can then be applied to semantic segmentation inferences without knowing the ground truth. In our experiments we use different LiDAR segmentation models and datasets and analyze the power of our method. We show that our results outperform other standard approaches.

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