{"total":73,"rows":[{"id":"2312.10035","title":"Point Transformer V3: Simpler, Faster, Stronger","created":"2023-12-15","cited_by_count":589,"area":null},{"id":"2503.16429","title":"Sonata: Self-Supervised Learning of Reliable Point Representations","created":"2025-03-20","cited_by_count":110,"area":null},{"id":"2404.07973","title":"Ferret-v2: An Improved Baseline for Referring and Grounding with Large Language Models","created":"2024-04-11","cited_by_count":97,"area":null},{"id":"2310.08370","title":"UniPAD: A Universal Pre-training Paradigm for Autonomous Driving","created":"2023-10-12","cited_by_count":73,"area":null},{"id":"2310.08586","title":"PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm","created":"2023-10-12","cited_by_count":56,"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":"2510.23607","title":"Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations","created":"2025-10-27","cited_by_count":45,"area":null},{"id":"2403.14418","title":"OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentation","created":"2024-03-21","cited_by_count":44,"area":null},{"id":"2503.18944","title":"DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation","created":"2025-03-24","cited_by_count":31,"area":null},{"id":"2312.03703","title":"Skeleton-in-Context: Unified Skeleton Sequence Modeling with In-Context Learning","created":"2023-12-06","cited_by_count":27,"area":null},{"id":"2403.09639","title":"GroupContrast: Semantic-aware Self-supervised Representation Learning for 3D Understanding","created":"2024-03-14","cited_by_count":26,"area":null},{"id":"2405.01538","title":"Multi-Space Alignments Towards Universal LiDAR Segmentation","created":"2024-05-02","cited_by_count":25,"area":null},{"id":"2502.02548","title":"Mosaic3D: Foundation Dataset and Model for Open-Vocabulary 3D Segmentation","created":"2025-02-04","cited_by_count":22,"area":null},{"id":"2603.03283","title":"Utonia: Toward One Encoder for All Point Clouds","created":"2026-03-03","cited_by_count":21,"area":null},{"id":"2404.10699","title":"ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentation","created":"2024-04-16","cited_by_count":10,"area":null},{"id":"2512.17817","title":"Chorus: Multi-Teacher Pretraining for Holistic 3D Gaussian Scene Encoding","created":"2025-12-19","cited_by_count":10,"area":null},{"id":"2603.16869","title":"SegviGen: Repurposing 3D Generative Model for Part Segmentation","created":"2026-03-17","cited_by_count":10,"area":null},{"id":"2408.17027","title":"ConDense: Consistent 2D/3D Pre-training for Dense and Sparse Features from Multi-View Images","created":"2024-08-30","cited_by_count":9,"area":null},{"id":"2506.18882","title":"Light of Normals: Unified Feature Representation for Universal Photometric Stereo","created":"2025-06-23","cited_by_count":9,"area":null},{"id":"2411.01584","title":"One for All: Multi-Domain Joint Training for Point Cloud Based 3D Object Detection","created":"2024-11-03","cited_by_count":8,"area":null},{"id":"2502.03901","title":"LeAP: Consistent multi-domain 3D labeling using Foundation Models","created":"2025-02-06","cited_by_count":8,"area":null},{"id":"2404.06700","title":"Scaling Multi-Camera 3D Object Detection through Weak-to-Strong Eliciting","created":"2024-04-10","cited_by_count":7,"area":null},{"id":"2410.13924","title":"ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding","created":"2024-10-17","cited_by_count":7,"area":null},{"id":"2502.20316","title":"Multi-Scale Neighborhood Occupancy Masked Autoencoder for Self-Supervised Learning in LiDAR Point Clouds","created":"2025-02-27","cited_by_count":7,"area":null},{"id":"2402.14215","title":"Swin3D++: Effective Multi-Source Pretraining for 3D Indoor Scene Understanding","created":"2024-02-22","cited_by_count":6,"area":null},{"id":"2402.18573","title":"Towards Unified 3D Object Detection via Algorithm and Data Unification","created":"2024-02-28","cited_by_count":6,"area":null},{"id":"2506.09952","title":"UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting","created":"2025-06-11","cited_by_count":6,"area":null},{"id":"2506.13766","title":"LHM++: An Efficient Large Human Reconstruction Model for Pose-free Images to 3D","created":"2025-06-16","cited_by_count":6,"area":null},{"id":"2503.12539","title":"BFANet: Revisiting 3D Semantic Segmentation with Boundary Feature Analysis","created":"2025-03-16","cited_by_count":5,"area":null},{"id":"2505.17099","title":"Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation","created":"2025-05-21","cited_by_count":5,"area":null},{"id":"2408.14253","title":"Text3DAug -- Prompted Instance Augmentation for LiDAR Perception","created":"2024-08-26","cited_by_count":4,"area":null},{"id":"2503.23980","title":"SALT: A Flexible Semi-Automatic Labeling Tool for General LiDAR Point Clouds with Cross-Scene Adaptability and 4D Consistency","created":"2025-03-31","cited_by_count":4,"area":null},{"id":"2505.22444","title":"On Geometry-Enhanced Parameter-Efficient Fine-Tuning for 3D Scene Segmentation","created":"2025-05-28","cited_by_count":4,"area":null},{"id":"2604.19609","title":"Volume Transformer: Revisiting Vanilla Transformers for 3D Scene Understanding","created":"2026-04-21","cited_by_count":4,"area":null},{"id":"2403.19220","title":"GeoAuxNet: Towards Universal 3D Representation Learning for Multi-sensor Point Clouds","created":"2024-03-28","cited_by_count":3,"area":null},{"id":"2409.20558","title":"Uni$^2$Det: Unified and Universal Framework for Prompt-Guided Multi-dataset 3D Detection","created":"2024-09-30","cited_by_count":3,"area":null},{"id":"2507.20170","title":"PUMPS: Skeleton-Agnostic Point-based Universal Motion Pre-Training for Synthesis in Human Motion Tasks","created":"2025-07-27","cited_by_count":3,"area":null},{"id":"2508.10897","title":"Human-in-Context: Unified Cross-Domain 3D Human Motion Modeling via In-Context Learning","created":"2025-08-14","cited_by_count":3,"area":null},{"id":"2508.02172","title":"GaussianCross: Cross-modal Self-supervised 3D Representation Learning via Gaussian Splatting","created":"2025-08-04","cited_by_count":2,"area":null},{"id":"2508.09470","title":"CitySeg: A 3D Open Vocabulary Semantic Segmentation Foundation Model in City-scale Scenarios","created":"2025-08-13","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":"2512.15708","title":"Multi-View Foundation Models","created":"2025-12-17","cited_by_count":2,"area":null},{"id":"2604.04933","title":"PointTPA: Dynamic Network Parameter Adaptation for 3D Scene Understanding","created":"2026-04-06","cited_by_count":2,"area":null},{"id":"2503.02372","title":"Label-Efficient LiDAR Panoptic Segmentation","created":"2025-03-04","cited_by_count":1,"area":null},{"id":"2503.21695","title":"AMA-SAM: Adversarial Multi-Domain Alignment of Segment Anything Model for High-Fidelity Histology Nuclei Segmentation","created":"2025-03-27","cited_by_count":1,"area":null},{"id":"2504.08217","title":"DrivAer Transformer: A high-precision and fast prediction method for vehicle aerodynamic drag coefficient based on the DrivAerNet++ dataset","created":"2025-04-11","cited_by_count":1,"area":null},{"id":"2508.00822","title":"Cross-Dataset Semantic Segmentation Performance Analysis: Unifying NIST Point Cloud City Datasets for 3D Deep Learning","created":"2025-08-01","cited_by_count":1,"area":null},{"id":"2605.10706","title":"RelFlexformer: Efficient Attention 3D-Transformers for Integrable Relative Positional Encodings","created":"2026-05-11","cited_by_count":1,"area":null},{"id":"2407.15282","title":"Point Transformer V3 Extreme: 1st Place Solution for 2024 Waymo Open Dataset Challenge in Semantic Segmentation","created":"2024-07-21","cited_by_count":0,"area":null},{"id":"2410.15569","title":"Online Pseudo-Label Unified Object Detection for Multiple Datasets Training","created":"2024-10-21","cited_by_count":0,"area":null},{"id":"2412.00489","title":"Density-aware global-local attention network for point cloud segmentation","created":"2024-11-30","cited_by_count":0,"area":null},{"id":"2504.12709","title":"Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving","created":"2025-04-17","cited_by_count":0,"area":null},{"id":"2506.06995","title":"Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems","created":"2025-06-08","cited_by_count":0,"area":null},{"id":"2508.20135","title":"Data-Efficient Point Cloud Semantic Segmentation Pipeline for Unimproved Roads","created":"2025-08-26","cited_by_count":0,"area":null},{"id":"2509.15886","title":"RangeSAM: On the Potential of Visual Foundation Models for Range-View represented LiDAR segmentation","created":"2025-09-19","cited_by_count":0,"area":null},{"id":"2510.10876","title":"rareboost3d: a synthetic lidar dataset with enhanced rare classes","created":"2025-10-13","cited_by_count":0,"area":null},{"id":"2510.11565","title":"SNAP: Towards Segmenting Anything in Any Point Cloud","created":"2025-10-13","cited_by_count":0,"area":null},{"id":"2510.25901","title":"BikeScenes: LiDAR Semantic Segmentation for Bicycles","created":"2025-10-29","cited_by_count":0,"area":null},{"id":"2511.00785","title":"Class-agnostic 3D Segmentation by Granularity-Consistent Automatic 2D Mask Tracking","created":"2025-11-02","cited_by_count":0,"area":null},{"id":"2512.23042","title":"3D sans 3D Scans: Scalable Pre-training from Video-Generated Point Clouds","created":"2025-12-28","cited_by_count":0,"area":null},{"id":"2604.17914","title":"Beyond Binary Contrast: Modeling Continuous Skeleton Action Spaces with Transitional Anchors","created":"2026-04-20","cited_by_count":0,"area":null},{"id":"2604.22658","title":"PASR: Pose-Aware 3D Shape Retrieval from Occluded Single Views","created":"2026-04-24","cited_by_count":0,"area":null},{"id":"2604.23095","title":"INSIGHT: Indoor Scene Intelligence from Geometric-Semantic Hierarchy Transfer for Public~Safety","created":"2026-04-25","cited_by_count":0,"area":null},{"id":"2605.04527","title":"Velox: Learning Representations of 4D Geometry and Appearance","created":"2026-05-06","cited_by_count":0,"area":null},{"id":"2606.06255","title":"RadiusFPS: Efficient Farthest Point Sampling on CPUs and GPUs via Spherical Voxel Pruning","created":"2026-06-04","cited_by_count":0,"area":null},{"id":"2608.07757","title":"Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark","created":"2026-08-07","cited_by_count":0,"area":null},{"id":"2608.17803","title":"Scale Matters: Adaptive Granularity Selection for Cross-Species 3D Plant Organ Segmentation","created":"2026-08-18","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},{"id":"2609.12473","title":"Partition-Invariant Tuning for 3D Scene Understanding","created":"2026-09-11","cited_by_count":0,"area":null},{"id":"2609.22716","title":"ZIL: Zero-shot Image-to-LiDAR Registration","created":"2026-09-19","cited_by_count":0,"area":null},{"id":"2609.28184","title":"VLMs Can Describe, But Not Measure: Object-Centric Scene Understanding for Robotic Manipulation","created":"2026-09-23","cited_by_count":0,"area":null},{"id":"2609.37264","title":"UniAfford: Token-Routed Multitask Learning for Generalizable 2D-3D Affordance Perception","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.13568","title":"Rationally Linearized MLPs","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}