Papers LIDAR Semantic Segmentation
“LIDAR Semantic Segmentation” 태그가 달린 논문 149편 · 필터 해제
No Adaptation Without Observation: Observability-Constrained Test-Time Prompt Tuning for LiDAR Semantic Segmentation
LiDAR semantic segmentation often degrades under real-world deployment due to evolving sensing conditions, while collecting new annotations for retraining is impractical. Test-time adaptation (TTA) updates model paramete…
LIDAR Semantic SegmentationTest-time AdaptationInvascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation
LiDAR semantic segmentation is a core perception capability for autonomous vehicles and mobile robots. However, safe operation also depends on knowing when predictions are unreliable. Existing approaches typically rely o…
LIDAR Semantic SegmentationAutonomous VehiclesCollaborative Learning for Semi-Supervised LiDAR Semantic Segmentation
Annotating large-scale LiDAR point clouds for 3D semantic segmentation is costly and time-consuming, which motivates the use of semi-supervised learning (SemiSL). Standard LiDAR SemiSL methods typically adopt a two-step …
LIDAR Semantic Segmentation3D Semantic SegmentationPoint CloudsOmniLiDAR: A Unified Diffusion Framework for Multi-Domain 3D LiDAR Generation
LiDAR scene generation is increasingly important for scalable simulation and synthetic data creation, especially under diverse sensing conditions that are costly to capture at scale. Typically, diffusion-based LiDAR gene…
LIDAR Semantic Segmentation3D Object DetectionData AugmentationScene GenerationFeasibility of Indoor Frame-Wise Lidar Semantic Segmentation via Distillation from Visual Foundation Model
Frame-wise semantic segmentation of indoor lidar scans is a fundamental step toward higher-level 3D scene understanding and mapping applications. However, acquiring frame-wise ground truth for training deep learning mode…
LIDAR Semantic SegmentationScene UnderstandingAutonomous DrivingHyperLiDAR: Adaptive Post-Deployment LiDAR Segmentation via Hyperdimensional Computing
LiDAR semantic segmentation plays a pivotal role in 3D scene understanding for edge applications such as autonomous driving. However, significant challenges remain for real-world deployments, particularly for on-device p…
LIDAR Semantic SegmentationScene UnderstandingAutonomous DrivingRePL: Pseudo-label Refinement for Semi-supervised LiDAR Semantic Segmentation
Semi-supervised learning for LiDAR semantic segmentation often suffers from error propagation and confirmation bias caused by noisy pseudo-labels. To tackle this chronic issue, we introduce RePL, a novel framework that e…
LIDAR Semantic SegmentationAdaptive Augmentation-Aware Latent Learning for Robust LiDAR Semantic Segmentation
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 robust…
LIDAR Semantic SegmentationCross-view Domain Generalization via Geometric Consistency for LiDAR Semantic Segmentation
Domain-generalized LiDAR semantic segmentation (LSS) seeks to train models on source-domain point clouds that generalize reliably to multiple unseen target domains, which is essential for real-world LiDAR applications. H…
LIDAR Semantic SegmentationDomain GeneralizationPoint CloudsUniFlow: Zero-Shot LiDAR Scene Flow for Autonomous Vehicles
LiDAR scene flow is the task of estimating per-point 3D motion between consecutive point clouds. Recent methods achieve centimeter-level accuracy on popular autonomous vehicle (AV) datasets, but are typically only traine…
LIDAR Semantic SegmentationAutonomous Vehicles3D Object DetectionPoint CloudsSource-Only Cross-Weather LiDAR via Geometry-Aware Point Drop
Adverse weather conditions, such as rain, snow, and fog, severely degrade LiDAR semantic segmentation by introducing refraction, scattering, and point dropouts that compromise geometric integrity. While prior approaches …
LIDAR Semantic SegmentationBikeScenes: Online LiDAR Semantic Segmentation for Bicycles
The vulnerability of cyclists, exacerbated by the rising popularity of faster e-bikes, motivates adapting automotive perception technologies for bicycle safety. We use our multi-sensor 'SenseBike' research platform to de…
LIDAR Semantic SegmentationDPGLA: Bridging the Gap between Synthetic and Real Data for Unsupervised Domain Adaptation in 3D LiDAR Semantic Segmentation
Annotating real-world LiDAR point clouds for use in intelligent autonomous systems is costly. To overcome this limitation, self-training-based Unsupervised Domain Adaptation (UDA) has been widely used to improve point cl…
Unsupervised Domain AdaptationLIDAR Semantic SegmentationData AugmentationPoint CloudsExploring Single Domain Generalization of LiDAR-based Semantic Segmentation under Imperfect Labels
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, …
LIDAR Semantic Segmentation3D Semantic SegmentationDomain GeneralizationImage ClassificationGeometry-Aware Cross Modal Alignment for Light Field-LiDAR Semantic Segmentation
Semantic segmentation serves as a cornerstone of scene understanding in autonomous driving but continues to face significant challenges under complex conditions such as occlusion. Light field and LiDAR modalities provide…
LIDAR Semantic SegmentationScene UnderstandingAutonomous DrivingPoint CloudsHARP-NeXt: High-Speed and Accurate Range-Point Fusion Network for 3D LiDAR Semantic Segmentation
LiDAR semantic segmentation is crucial for autonomous vehicles and mobile robots, requiring high accuracy and real-time processing, especially on resource-constrained embedded systems. Previous state-of-the-art methods o…
LIDAR Semantic SegmentationAutonomous VehiclesOut-of-Distribution Detection in LiDAR Semantic Segmentation Using Epistemic Uncertainty from Hierarchical GMMs
In addition to accurate scene understanding through precise semantic segmentation of LiDAR point clouds, detecting out-of-distribution (OOD) objects, instances not encountered during training, is essential to prevent the…
Out-of-Distribution DetectionLIDAR Semantic SegmentationScene UnderstandingPoint CloudsPoint-Plane Projections for Accurate LiDAR Semantic Segmentation in Small Data Scenarios
LiDAR point cloud semantic segmentation is essential for interpreting 3D environments in applications such as autonomous driving and robotics. Recent methods achieve strong performance by exploiting different point cloud…
LIDAR Semantic SegmentationAutonomous DrivingData AugmentationVeila: Panoramic LiDAR Generation from a Monocular RGB Image
Realistic and controllable panoramic LiDAR data generation is critical for scalable 3D perception in autonomous driving and robotics. Existing methods either perform unconditional generation with poor controllability or …
LIDAR Semantic SegmentationAutonomous DrivingData AugmentationUnsupervised Domain Adaptation for 3D LiDAR Semantic Segmentation Using Contrastive Learning and Multi-Model Pseudo Labeling
Addressing performance degradation in 3D LiDAR semantic segmentation due to domain shifts (e.g., sensor type, geographical location) is crucial for autonomous systems, yet manual annotation of target data is prohibitive.…
Unsupervised Domain AdaptationLIDAR Semantic SegmentationContrastive Learning