{"total":166,"rows":[{"id":"2312.10035","title":"Point Transformer V3: Simpler, Faster, Stronger","created":"2023-12-15","cited_by_count":589,"area":null},{"id":"2302.07817","title":"Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction","created":"2023-02-15","cited_by_count":409,"area":null},{"id":"2304.14365","title":"Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving","created":"2023-04-27","cited_by_count":332,"area":"wm"},{"id":"2304.05316","title":"OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction","created":"2023-04-11","cited_by_count":293,"area":null},{"id":"2303.03991","title":"OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception","created":"2023-03-07","cited_by_count":221,"area":null},{"id":"2306.02851","title":"Scene as Occupancy","created":"2023-06-05","cited_by_count":209,"area":null},{"id":"2301.04926","title":"CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP","created":"2023-01-12","cited_by_count":181,"area":null},{"id":"2311.01043","title":"LLM4Drive: A Survey of Large Language Models for Autonomous Driving","created":"2023-11-02","cited_by_count":136,"area":"driving"},{"id":"2303.17597","title":"Robo3D: Towards Robust and Reliable 3D Perception against Corruptions","created":"2023-03-30","cited_by_count":129,"area":null},{"id":"2303.05367","title":"Rethinking Range View Representation for LiDAR Segmentation","created":"2023-03-09","cited_by_count":128,"area":"wm"},{"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":"2306.10013","title":"PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation","created":"2023-06-16","cited_by_count":115,"area":null},{"id":"2207.00026","title":"LaserMix for Semi-Supervised LiDAR Semantic Segmentation","created":"2022-06-30","cited_by_count":93,"area":null},{"id":"2312.02934","title":"WoVoGen: World Volume-aware Diffusion for Controllable Multi-camera Driving Scene Generation","created":"2023-12-05","cited_by_count":93,"area":null},{"id":"2306.09001","title":"SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Driving","created":"2023-06-15","cited_by_count":75,"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":"2209.09385","title":"LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perception","created":"2022-09-19","cited_by_count":74,"area":null},{"id":"2404.00815","title":"Towards Realistic Scene Generation with LiDAR Diffusion Models","created":"2024-03-31","cited_by_count":69,"area":"wm"},{"id":"2303.08600","title":"MSeg3D: Multi-modal 3D Semantic Segmentation for Autonomous Driving","created":"2023-03-15","cited_by_count":68,"area":null},{"id":"2412.04383","title":"SeeGround: See and Ground for Zero-Shot Open-Vocabulary 3D Visual Grounding","created":"2024-12-05","cited_by_count":67,"area":null},{"id":"2509.07996","title":"3D and 4D World Modeling: A Survey","created":"2025-09-04","cited_by_count":67,"area":"wm"},{"id":"2401.12425","title":"The Neglected Tails in Vision-Language Models","created":"2024-01-23","cited_by_count":63,"area":"mllm"},{"id":"2205.07002","title":"Panoptic-PHNet: Towards Real-Time and High-Precision LiDAR Panoptic Segmentation via Clustering Pseudo Heatmap","created":"2022-05-14","cited_by_count":59,"area":null},{"id":"2309.05573","title":"UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase","created":"2023-09-11","cited_by_count":57,"area":null},{"id":"2306.03899","title":"Towards Label-free Scene Understanding by Vision Foundation Models","created":"2023-06-06","cited_by_count":54,"area":"mllm"},{"id":"2405.05258","title":"Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving","created":"2024-05-08","cited_by_count":54,"area":null},{"id":"2312.04484","title":"FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation","created":"2023-12-07","cited_by_count":50,"area":null},{"id":"2403.13129","title":"Better Call SAL: Towards Learning to Segment Anything in Lidar","created":"2024-03-19","cited_by_count":47,"area":null},{"id":"2304.00690","title":"3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds","created":"2023-04-03","cited_by_count":46,"area":null},{"id":"2111.15242","title":"ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation","created":"2021-11-30","cited_by_count":41,"area":null},{"id":"2403.13470","title":"Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion","created":"2024-03-20","cited_by_count":40,"area":"wm"},{"id":"2308.00353","title":"Lowis3D: Language-Driven Open-World Instance-Level 3D Scene Understanding","created":"2023-08-01","cited_by_count":38,"area":null},{"id":"2405.04299","title":"ViewFormer: Exploring Spatiotemporal Modeling for Multi-View 3D Occupancy Perception via View-Guided Transformers","created":"2024-05-07","cited_by_count":37,"area":null},{"id":"2209.14858","title":"4D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and Aggregation","created":"2022-09-29","cited_by_count":35,"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":"2312.04316","title":"Towards Knowledge-driven Autonomous Driving","created":"2023-12-07","cited_by_count":29,"area":"driving"},{"id":"2203.10926","title":"3D Multi-Object Tracking Using Graph Neural Networks with Cross-Edge Modality Attention","created":"2022-03-21","cited_by_count":27,"area":null},{"id":"2403.17009","title":"Is Your LiDAR Placement Optimized for 3D Scene Understanding?","created":"2024-03-25","cited_by_count":26,"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":"2405.01538","title":"Multi-Space Alignments Towards Universal LiDAR Segmentation","created":"2024-05-02","cited_by_count":25,"area":null},{"id":"2309.16133","title":"Mask4Former: Mask Transformer for 4D Panoptic Segmentation","created":"2023-09-28","cited_by_count":24,"area":null},{"id":"2502.17288","title":"GaussianFlowOcc: Sparse and Weakly Supervised Occupancy Estimation using Gaussian Splatting and Temporal Flow","created":"2025-02-24","cited_by_count":24,"area":null},{"id":"2411.16308","title":"An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion Models","created":"2024-11-25","cited_by_count":23,"area":"diffusion"},{"id":"2402.12792","title":"OccFlowNet: Towards Self-supervised Occupancy Estimation via Differentiable Rendering and Occupancy Flow","created":"2024-02-20","cited_by_count":22,"area":null},{"id":"2304.03251","title":"SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation","created":"2023-04-06","cited_by_count":21,"area":null},{"id":"2302.06175","title":"Learning and Aggregating Lane Graphs for Urban Automated Driving","created":"2023-02-13","cited_by_count":20,"area":null},{"id":"2310.08820","title":"Learning to Adapt SAM for Segmenting Cross-domain Point Clouds","created":"2023-10-13","cited_by_count":20,"area":null},{"id":"2109.15286","title":"Unsupervised Domain Adaptation for LiDAR Panoptic Segmentation","created":"2021-09-30","cited_by_count":19,"area":null},{"id":"2303.15651","title":"4D Panoptic Segmentation as Invariant and Equivariant Field Prediction","created":"2023-03-28","cited_by_count":19,"area":null},{"id":"2501.04004","title":"LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes","created":"2025-01-07","cited_by_count":19,"area":"moe"},{"id":"2308.01686","title":"LiDAR-Camera Panoptic Segmentation via Geometry-Consistent and Semantic-Aware Alignment","created":"2023-08-03","cited_by_count":18,"area":null},{"id":"2303.13509","title":"Position-Guided Point Cloud Panoptic Segmentation Transformer","created":"2023-03-23","cited_by_count":17,"area":null},{"id":"2309.05448","title":"Panoptic Vision-Language Feature Fields","created":"2023-09-11","cited_by_count":17,"area":null},{"id":"2302.06185","title":"PUPS: Point Cloud Unified Panoptic Segmentation","created":"2023-02-13","cited_by_count":16,"area":null},{"id":"2303.14378","title":"Instant Domain Augmentation for LiDAR Semantic Segmentation","created":"2023-03-25","cited_by_count":16,"area":null},{"id":"2111.01723","title":"CPSeg: Cluster-free Panoptic Segmentation of 3D LiDAR Point Clouds","created":"2021-11-02","cited_by_count":14,"area":null},{"id":"2308.13323","title":"SVQNet: Sparse Voxel-Adjacent Query Network for 4D Spatio-Temporal LiDAR Semantic Segmentation","created":"2023-08-25","cited_by_count":14,"area":null},{"id":"2401.06704","title":"Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering","created":"2024-01-12","cited_by_count":14,"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":"2307.15061","title":"The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation","created":"2023-07-27","cited_by_count":13,"area":null},{"id":"2311.01520","title":"4D-Former: Multimodal 4D Panoptic Segmentation","created":"2023-11-02","cited_by_count":12,"area":null},{"id":"2312.11837","title":"Regulating Intermediate 3D Features for Vision-Centric Autonomous Driving","created":"2023-12-19","cited_by_count":12,"area":null},{"id":"2405.14870","title":"An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models","created":"2024-05-23","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":"2511.01755","title":"3EED: Ground Everything Everywhere in 3D","created":"2025-11-03","cited_by_count":12,"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":"2404.01686","title":"JRDB-PanoTrack: An Open-world Panoptic Segmentation and Tracking Robotic Dataset in Crowded Human Environments","created":"2024-04-02","cited_by_count":11,"area":null},{"id":"2211.10023","title":"LiSnowNet: Real-time Snow Removal for LiDAR Point Cloud","created":"2022-11-18","cited_by_count":10,"area":null},{"id":"2302.08292","title":"Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles","created":"2023-02-16","cited_by_count":9,"area":null},{"id":"2302.09043","title":"Self-Supervised Representation Learning from Temporal Ordering of Automated Driving Sequences","created":"2023-02-17","cited_by_count":9,"area":null},{"id":"2308.13794","title":"SOGDet: Semantic-Occupancy Guided Multi-view 3D Object Detection","created":"2023-08-26","cited_by_count":9,"area":null},{"id":"2309.07471","title":"EP2P-Loc: End-to-End 3D Point to 2D Pixel Localization for Large-Scale Visual Localization","created":"2023-09-14","cited_by_count":9,"area":null},{"id":"2401.16305","title":"MixSup: Mixed-grained Supervision for Label-efficient LiDAR-based 3D Object Detection","created":"2024-01-29","cited_by_count":9,"area":null},{"id":"2404.12798","title":"A Point-Based Approach to Efficient LiDAR Multi-Task Perception","created":"2024-04-19","cited_by_count":9,"area":null},{"id":"2412.07385","title":"LOGen: Toward Lidar Object Generation by Point Diffusion","created":"2024-12-10","cited_by_count":9,"area":"wm"},{"id":"2206.11428","title":"LidarMultiNet: Unifying LiDAR Semantic Segmentation, 3D Object Detection, and Panoptic Segmentation in a Single Multi-task Network","created":"2022-06-23","cited_by_count":8,"area":null},{"id":"2212.14750","title":"Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time Series","created":"2022-12-30","cited_by_count":8,"area":null},{"id":"2304.13980","title":"A Review of Panoptic Segmentation for Mobile Mapping Point Clouds","created":"2023-04-27","cited_by_count":8,"area":null},{"id":"2307.02877","title":"Towards accurate instance segmentation in large-scale LiDAR point clouds","created":"2023-07-06","cited_by_count":8,"area":null},{"id":"2310.08035","title":"BaSAL: Size-Balanced Warm Start Active Learning for LiDAR Semantic Segmentation","created":"2023-10-12","cited_by_count":8,"area":null},{"id":"2311.07761","title":"Amodal Optical Flow","created":"2023-11-13","cited_by_count":8,"area":null},{"id":"2409.14273","title":"Lidar Panoptic Segmentation in an Open World","created":"2024-09-22","cited_by_count":8,"area":null},{"id":"2412.08243","title":"Hierarchical Context Alignment with Disentangled Geometric and Temporal Modeling for Semantic Occupancy Prediction","created":"2024-12-11","cited_by_count":8,"area":null},{"id":"2505.18956","title":"How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation","created":"2025-05-25","cited_by_count":8,"area":null},{"id":"2603.19979","title":"X-World: Controllable Ego-Centric Multi-Camera World Models for Scalable End-to-End Driving","created":"2026-03-20","cited_by_count":8,"area":"wm"},{"id":"2208.04510","title":"Unsupervised Domain Adaptation for Point Cloud Semantic Segmentation via Graph Matching","created":"2022-08-09","cited_by_count":7,"area":null},{"id":"2304.13147","title":"Self-Supervised Multi-Object Tracking For Autonomous Driving From Consistency Across Timescales","created":"2023-04-25","cited_by_count":7,"area":null},{"id":"2309.13276","title":"Discwise Active Learning for LiDAR Semantic Segmentation","created":"2023-09-23","cited_by_count":7,"area":null},{"id":"2310.07591","title":"PeP: a Point enhanced Painting method for unified point cloud tasks","created":"2023-10-11","cited_by_count":7,"area":null},{"id":"2310.12464","title":"Lidar Panoptic Segmentation and Tracking without Bells and Whistles","created":"2023-10-19","cited_by_count":7,"area":null},{"id":"2312.07221","title":"Transferring CLIP's Knowledge into Zero-Shot Point Cloud Semantic Segmentation","created":"2023-12-12","cited_by_count":7,"area":null},{"id":"2504.12264","title":"Towards Learning to Complete Anything in Lidar","created":"2025-04-16","cited_by_count":7,"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":"2607.06560","title":"Vision as Unified Multimodal Generation","created":"2026-07-07","cited_by_count":7,"area":null},{"id":"2311.09499","title":"Center Focusing Network for Real-Time LiDAR Panoptic Segmentation","created":"2023-11-16","cited_by_count":6,"area":null},{"id":"2312.08234","title":"Beyond the Label Itself: Latent Labels Enhance Semi-supervised Point Cloud Panoptic Segmentation","created":"2023-12-13","cited_by_count":6,"area":null},{"id":"2504.00848","title":"Zero-Shot 4D Lidar Panoptic Segmentation","created":"2025-04-01","cited_by_count":6,"area":null},{"id":"2506.13265","title":"Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learning","created":"2025-06-16","cited_by_count":6,"area":null},{"id":"2511.15396","title":"ShelfOcc: Native 3D Supervision beyond LiDAR for Vision-Based Occupancy Estimation","created":"2025-11-19","cited_by_count":6,"area":null},{"id":"2602.19349","title":"UP-Fuse: Uncertainty-guided LiDAR-Camera Fusion for 3D Panoptic Segmentation","created":"2026-02-22","cited_by_count":6,"area":null}],"next_cursor":"100"}