{"total":32,"rows":[{"id":"2303.15361","title":"A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts","created":"2023-03-27","cited_by_count":359,"area":null},{"id":"2306.09347","title":"Segment Any Point Cloud Sequences by Distilling Vision Foundation Models","created":"2023-06-15","cited_by_count":125,"area":null},{"id":"2208.00223","title":"PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds","created":"2022-07-30","cited_by_count":74,"area":null},{"id":"2310.20199","title":"In Search of Lost Online Test-time Adaptation: A Survey","created":"2023-10-31","cited_by_count":44,"area":null},{"id":"2407.06190","title":"4D Contrastive Superflows are Dense 3D Representation Learners","created":"2024-07-08","cited_by_count":33,"area":null},{"id":"2212.04245","title":"Domain generalization of 3D semantic segmentation in autonomous driving","created":"2022-12-07","cited_by_count":25,"area":null},{"id":"2305.19812","title":"A Survey of Label-Efficient Deep Learning for 3D Point Clouds","created":"2023-05-31","cited_by_count":20,"area":null},{"id":"2308.14619","title":"Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation","created":"2023-08-28","cited_by_count":20,"area":null},{"id":"2303.14378","title":"Instant Domain Augmentation for LiDAR Semantic Segmentation","created":"2023-03-25","cited_by_count":16,"area":null},{"id":"2304.11705","title":"Walking Your LiDOG: A Journey Through Multiple Domains for LiDAR Semantic Segmentation","created":"2023-04-23","cited_by_count":13,"area":null},{"id":"2309.06197","title":"360$^\\circ$ from a Single Camera: A Few-Shot Approach for LiDAR Segmentation","created":"2023-09-12","cited_by_count":12,"area":null},{"id":"2501.04005","title":"LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving","created":"2025-01-07","cited_by_count":12,"area":null},{"id":"2310.20293","title":"Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation","created":"2023-10-31","cited_by_count":11,"area":null},{"id":"2403.17010","title":"Calib3D: Calibrating Model Preferences for Reliable 3D Scene Understanding","created":"2024-03-25","cited_by_count":11,"area":null},{"id":"2311.03017","title":"COLA: COarse-LAbel multi-source LiDAR semantic segmentation for autonomous driving","created":"2023-11-06","cited_by_count":8,"area":null},{"id":"2507.05260","title":"Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations","created":"2025-07-07","cited_by_count":7,"area":null},{"id":"2407.12387","title":"HGL: Hierarchical Geometry Learning for Test-time Adaptation in 3D Point Cloud Segmentation","created":"2024-07-17","cited_by_count":6,"area":null},{"id":"2409.04409","title":"Train Till You Drop: Towards Stable and Robust Source-free Unsupervised 3D Domain Adaptation","created":"2024-09-06","cited_by_count":6,"area":null},{"id":"2308.02883","title":"Cross-modal & Cross-domain Learning for Unsupervised LiDAR Semantic Segmentation","created":"2023-08-05","cited_by_count":4,"area":null},{"id":"2312.03782","title":"Novel class discovery meets foundation models for 3D semantic segmentation","created":"2023-12-06","cited_by_count":4,"area":null},{"id":"2408.14253","title":"Text3DAug -- Prompted Instance Augmentation for LiDAR Perception","created":"2024-08-26","cited_by_count":4,"area":null},{"id":"2503.15106","title":"Distilling 3D distinctive local descriptors for 6D pose estimation","created":"2025-03-19","cited_by_count":4,"area":null},{"id":"2503.18384","title":"LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectives","created":"2025-03-24","cited_by_count":4,"area":null},{"id":"2505.09971","title":"APCoTTA: Continual Test-Time Adaptation for Semantic Segmentation of Airborne LiDAR Point Clouds","created":"2025-05-15","cited_by_count":2,"area":null},{"id":"2511.17455","title":"Improving Multimodal Distillation for 3D Semantic Segmentation under Domain Shift","created":"2025-11-21","cited_by_count":2,"area":null},{"id":"2308.14005","title":"Calibrating Panoramic Depth Estimation for Practical Localization and Mapping","created":"2023-08-27","cited_by_count":1,"area":null},{"id":"2403.06461","title":"Interactive Test-Time Adaptation with Reliable Spatial-Temporal Voxels for Multi-Modal Segmentation","created":"2024-03-11","cited_by_count":1,"area":null},{"id":"2407.07465","title":"Exploring the Untouched Sweeps for Conflict-Aware 3D Segmentation Pretraining","created":"2024-07-10","cited_by_count":1,"area":null},{"id":"2503.08601","title":"LiSu: A Dataset and Method for LiDAR Surface Normal Estimation","created":"2025-03-11","cited_by_count":1,"area":null},{"id":"2606.30937","title":"No Adaptation Without Observation: Observability-Constrained Test-Time Prompt Tuning for LiDAR Semantic Segmentation","created":"2026-06-29","cited_by_count":0,"area":null},{"id":"2608.02883","title":"Test Time Adaptation Methods for Point Cloud Registration in Laparoscopic Surgery","created":"2026-08-03","cited_by_count":0,"area":null},{"id":"2608.18479","title":"COSTA: A Cluster-Centric Paradigm for Annotation-Free Open-Set Semantic Segmentation of Aerial Point Clouds with Domain Shifts","created":"2026-08-19","cited_by_count":0,"area":null}],"next_cursor":null}