Adaptive Augmentation-Aware Latent Learning for Robust LiDAR Semantic Segmentation

2026-03-01

AI Summary

This work addresses the significant performance degradation of LiDAR point cloud semantic segmentation under adverse weather conditions, a challenge exacerbated by existing data augmentation methods that struggle to balance mild and strong perturbations while often inducing semantic shifts. To overcome this, the authors propose A3Point, a novel framework that decouples semantic confusion from semantic shift. Specifically, it introduces a Semantic Confusion Prior (SCP) to implicitly model the semantic uncertainty induced by augmentation, coupled with a Semantic Shift Region (SSR) localization mechanism that adaptively selects optimal augmentation strategies in the latent space. This augmentation-aware training paradigm achieves state-of-the-art performance across multiple LiDAR segmentation benchmarks under adverse weather, substantially enhancing model robustness to distributional shifts.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: Multimodal Perception & Sensor Fusion

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: Large language models for search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Adverse weather conditions significantly degrade the performance of LiDAR point cloud semantic segmentation networks by introducing large distribution shifts. Existing augmentation-based methods attempt to enhance robustness by simulating weather interference during training. However, they struggle to fully exploit the potential of augmentations due to the trade-off between minor and aggressive augmentations. To address this, we propose A3Point, an adaptive augmentation-aware latent learning framework that effectively utilizes a diverse range of augmentations while mitigating the semantic shift, which refers to the change in the semantic meaning caused by augmentations. A3Point consists of two key components: semantic confusion prior (SCP) latent learning, which captures the model's inherent semantic confusion information, and semantic shift region (SSR) localization, which decouples semantic confusion and semantic shift, enabling adaptive optimization strategies for different disturbance levels. Extensive experiments on multiple standard generalized LiDAR segmentation benchmarks under adverse weather demonstrate the effectiveness of our method, setting new state-of-the-art results.

Problem

• LiDAR semantic segmentation
• adverse weather
• data augmentation
• distribution shift
• semantic shift

Innovation

• adaptive augmentation
• semantic shift
• latent learning
• LiDAR semantic segmentation
• robustness