Exploring Single Domain Generalization of LiDAR-based Semantic Segmentation under Imperfect Labels

• URL: http://arxiv.org/abs/2510.09035v1
• Date: Fri, 10 Oct 2025 06:11:34 GMT
• Title: Exploring Single Domain Generalization of LiDAR-based Semantic Segmentation under Imperfect Labels
• Authors: Weitong Kong, Zichao Zeng, Di Wen, Jiale Wei, Kunyu Peng, June Moh Goo, Jan Boehm, Rainer Stiefelhagen,
• Abstract summary: We introduce the novel task Domain Generalization for LiDAR under Noisy Labels (DGLSS-NL)<n>We find that existing noisy-label learning approaches adapt poorly to LiDAR data.<n>We propose DuNe, a dual-view framework with strong and weak branches that enforce feature-level consistency and apply cross-entropy loss based on confidence-aware filtering of predictions.
• Score: 28.96799571666756
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate perception is critical for vehicle safety, with LiDAR as a key enabler in autonomous driving. To ensure robust performance across environments, sensor types, and weather conditions without costly re-annotation, domain generalization in LiDAR-based 3D semantic segmentation is essential. However, LiDAR annotations are often noisy due to sensor imperfections, occlusions, and human errors. Such noise degrades segmentation accuracy and is further amplified under domain shifts, threatening system reliability. While noisy-label learning is well-studied in images, its extension to 3D LiDAR segmentation under domain generalization remains largely unexplored, as the sparse and irregular structure of point clouds limits direct use of 2D methods. To address this gap, we introduce the novel task Domain Generalization for LiDAR Semantic Segmentation under Noisy Labels (DGLSS-NL) and establish the first benchmark by adapting three representative noisy-label learning strategies from image classification to 3D segmentation. However, we find that existing noisy-label learning approaches adapt poorly to LiDAR data. We therefore propose DuNe, a dual-view framework with strong and weak branches that enforce feature-level consistency and apply cross-entropy loss based on confidence-aware filtering of predictions. Our approach shows state-of-the-art performance by achieving 56.86% mIoU on SemanticKITTI, 42.28% on nuScenes, and 52.58% on SemanticPOSS under 10% symmetric label noise, with an overall Arithmetic Mean (AM) of 49.57% and Harmonic Mean (HM) of 48.50%, thereby demonstrating robust domain generalization in DGLSS-NL tasks. The code is available on our project page.

Related papers

• Bridging the Modality Gap in Roadside LiDAR: A Training-Free Vision-Language Model Framework for Vehicle Classification [5.746505534720594]
  Fine-grained truck classification is critical for intelligent transportation systems (ITS)<n>Current LiDAR-based methods face scalability challenges due to their reliance on supervised deep learning and labor-intensive manual annotation.<n>We propose a framework that bridges this gap by adapting off-the-shelf Vision-Language Models for fine-grained truck classification without parameter fine-tuning.
  arXiv  Detail & Related papers  (2026-02-10T05:39:48Z)
• ALISE: Annotation-Free LiDAR Instance Segmentation for Autonomous Driving [9.361724251990154]
  We introduce ALISE, a novel framework that performs LiDAR instance segmentation without any annotations.<n>Our approach starts by employing Vision Foundation Models (VFMs), guided by text and images, to produce initial pseudo-labels.<n>We then refine these labels through a dedicated manual-temporal voting module, which combines 2D and 3D semantics for both offline and online optimization.<n>This comprehensive design results in significant performance gains, establishing a new state-of-the-art for unsupervised 3D instance segmentation.
  arXiv  Detail & Related papers  (2025-10-07T10:15:18Z)
• Unsupervised Domain Adaptation for 3D LiDAR Semantic Segmentation Using Contrastive Learning and Multi-Model Pseudo Labeling [0.7373617024876725]
  Unsupervised contrastive learning at the segment level is used to pre-train a backbone network.<n>A multi-model pseudo-labeling strategy is introduced, utilizing an ensemble of diverse state-of-the-art architectures.<n>Experiments adapting from Semantic KITTI to unlabeled target datasets demonstrate significant improvements in segmentation accuracy.
  arXiv  Detail & Related papers  (2025-07-24T08:21:43Z)
• Towards Modality-agnostic Label-efficient Segmentation with Entropy-Regularized Distribution Alignment [62.73503467108322]
  This topic is widely studied in 3D point cloud segmentation due to the difficulty of annotating point clouds densely. Until recently, pseudo-labels have been widely employed to facilitate training with limited ground-truth labels. Existing pseudo-labeling approaches could suffer heavily from the noises and variations in unlabelled data. We propose a novel learning strategy to regularize the pseudo-labels generated for training, thus effectively narrowing the gaps between pseudo-labels and model predictions.
  arXiv  Detail & Related papers  (2024-08-29T13:31:15Z)
• 2D Feature Distillation for Weakly- and Semi-Supervised 3D Semantic Segmentation [92.17700318483745]
  We propose an image-guidance network (IGNet) which builds upon the idea of distilling high level feature information from a domain adapted synthetically trained 2D semantic segmentation network. IGNet achieves state-of-the-art results for weakly-supervised LiDAR semantic segmentation on ScribbleKITTI, boasting up to 98% relative performance to fully supervised training with only 8% labeled points.
  arXiv  Detail & Related papers  (2023-11-27T07:57:29Z)
• All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation [67.30502812804271]
  Pseudo-labels are widely employed in weakly supervised 3D segmentation tasks where only sparse ground-truth labels are available for learning. We propose a novel learning strategy to regularize the generated pseudo-labels and effectively narrow the gaps between pseudo-labels and model predictions.
  arXiv  Detail & Related papers  (2023-05-25T08:19:31Z)
• Benchmarking the Robustness of LiDAR Semantic Segmentation Models [78.6597530416523]
  In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models under various corruptions. We propose a new benchmark called SemanticKITTI-C, which features 16 out-of-domain LiDAR corruptions in three groups, namely adverse weather, measurement noise and cross-device discrepancy. We design a robust LiDAR segmentation model (RLSeg) which greatly boosts the robustness with simple but effective modifications.
  arXiv  Detail & Related papers  (2023-01-03T06:47:31Z)
• Exploiting Negative Learning for Implicit Pseudo Label Rectification in Source-Free Domain Adaptive Semantic Segmentation [12.716865774780704]
  State-of-the-art methods for source free domain adaptation (SFDA) are subject to strict limits. textitPR-SFDA achieves a performance of 49.0 mIoU, which is very close to that of the state-of-the-art counterparts.
  arXiv  Detail & Related papers  (2021-06-23T02:20:31Z)
• 3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection [76.42897462051067]
  3DIoUMatch is a novel semi-supervised method for 3D object detection applicable to both indoor and outdoor scenes. We leverage a teacher-student mutual learning framework to propagate information from the labeled to the unlabeled train set in the form of pseudo-labels. Our method consistently improves state-of-the-art methods on both ScanNet and SUN-RGBD benchmarks by significant margins under all label ratios.
  arXiv  Detail & Related papers  (2020-12-08T11:06:26Z)
• Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds [49.47017280475232]
  We study an unsupervised domain adaptation problem for the semantic labeling of 3D point clouds. We take a Complete and Label approach to recover the underlying surfaces before passing them to a segmentation network. The recovered 3D surfaces serve as a canonical domain, from which semantic labels can transfer across different LiDAR sensors.
  arXiv  Detail & Related papers  (2020-07-16T17:42:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.