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

TL;DR

This paper presents a sampling-free method for producing well-calibrated confidence estimates in LiDAR scene semantic segmentation, enhancing safety and efficiency in autonomous driving applications.

Contribution

It introduces a novel sampling-free approach that achieves reliable confidence calibration and faster inference compared to traditional sampling-based methods.

Findings

Maintains well-calibrated confidence values with increased processing speed.

Produces underconfident predictions, beneficial for safety-critical tasks.

Outperforms sampling-based methods in calibration and efficiency.

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.