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

2024-11-18

arXiv.org

AI Summary

To address the real-time, trustworthy inference requirements for LiDAR point cloud semantic segmentation in autonomous driving, existing sampling-based uncertainty estimation methods suffer from slow inference and poor calibration. This paper proposes the first sampling-agnostic confidence calibration framework, comprising an adaptive calibration error (ACE)-optimized deterministic confidence mapping, confidence–accuracy consistency modeling, and a lightweight post-processing module—enabling efficient, systematically underconfident calibration. The method ensures robustness in safety-critical scenarios while significantly improving efficiency: ACE is reduced by 42% and inference speed increases by 8.3×. Reliability diagrams empirically validate its stable underconfidence behavior, and comprehensive calibration metrics demonstrate superior performance over Monte Carlo sampling baselines.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Agentic search
• Security and Privacy: Large-scale security measurements

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.

Problem

• Estimating well-calibrated confidence for LiDAR segmentation
• Reducing inference time in sampling-free confidence methods
• Ensuring underconfidence for safety-critical autonomous driving

Innovation

• Sampling-free confidence estimation for LiDAR segmentation
• Achieves alignment with true classification accuracy
• Reduces inference time compared to sampling methods