Calibrated and Efficient Sampling-Free Confidence Estimation for LiDAR Scene Semantic Segmentation

• URL: http://arxiv.org/abs/2411.11935v1
• Date: Mon, 18 Nov 2024 15:13:20 GMT
• Title: Calibrated and Efficient Sampling-Free Confidence Estimation for LiDAR Scene Semantic Segmentation
• Authors: Hanieh Shojaei Miandashti, Qianqian Zou, Claus Brenner,
• Abstract summary: We introduce a sampling-free approach for estimating well-calibrated confidence values for classification tasks. Our approach maintains well-calibrated confidence values while achieving increased processing speed. Our method produces underconfidence rather than overconfident predictions, an advantage for safety-critical applications.
• Score: 1.8861801513235323
• License:
• Abstract: Reliable deep learning models require not only accurate predictions but also well-calibrated confidence estimates to ensure dependable uncertainty estimation. This is crucial in safety-critical applications like autonomous driving, which depend on rapid and precise semantic segmentation of LiDAR point clouds for real-time 3D scene understanding. In this work, we introduce a sampling-free approach for estimating well-calibrated confidence values for classification tasks, achieving alignment with true classification accuracy and significantly reducing inference time compared to sampling-based methods. Our evaluation using the Adaptive Calibration Error (ACE) metric for LiDAR semantic segmentation shows that our approach maintains well-calibrated confidence values while achieving increased processing speed compared to a sampling baseline. Additionally, reliability diagrams reveal that our method produces underconfidence rather than overconfident predictions, an advantage for safety-critical applications. Our sampling-free approach offers well-calibrated and time-efficient predictions for LiDAR scene semantic segmentation.

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