{"total":59,"rows":[{"id":"2303.05367","title":"Rethinking Range View Representation for LiDAR Segmentation","created":"2023-03-09","cited_by_count":128,"area":"wm"},{"id":"2110.02210","title":"Mix3D: Out-of-Context Data Augmentation for 3D Scenes","created":"2021-10-05","cited_by_count":126,"area":null},{"id":"2201.12296","title":"Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions","created":"2022-01-28","cited_by_count":80,"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":"2202.03377","title":"Benchmarking and Analyzing Point Cloud Classification under Corruptions","created":"2022-02-07","cited_by_count":70,"area":null},{"id":"2311.05709","title":"OmniVec: Learning robust representations with cross modal sharing","created":"2023-11-07","cited_by_count":44,"area":null},{"id":"2106.04180","title":"Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models","created":"2021-06-08","cited_by_count":41,"area":null},{"id":"2312.15742","title":"DI-V2X: Learning Domain-Invariant Representation for Vehicle-Infrastructure Collaborative 3D Object Detection","created":"2023-12-25","cited_by_count":34,"area":null},{"id":"2205.07417","title":"Transformers in 3D Point Clouds: A Survey","created":"2022-05-16","cited_by_count":31,"area":null},{"id":"2212.10888","title":"A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability","created":"2022-12-21","cited_by_count":31,"area":null},{"id":"2106.07085","title":"Survey: Image Mixing and Deleting for Data Augmentation","created":"2021-06-13","cited_by_count":26,"area":null},{"id":"2308.12113","title":"Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey","created":"2023-08-23","cited_by_count":24,"area":null},{"id":"2210.12755","title":"LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformers","created":"2022-10-23","cited_by_count":21,"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":"2409.05202","title":"A Survey on Mixup Augmentations and Beyond","created":"2024-09-08","cited_by_count":20,"area":null},{"id":"2303.04599","title":"Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space","created":"2023-03-08","cited_by_count":19,"area":null},{"id":"2304.01537","title":"PartMix: Regularization Strategy to Learn Part Discovery for Visible-Infrared Person Re-identification","created":"2023-04-04","cited_by_count":19,"area":null},{"id":"2412.09511","title":"GEAL: Generalizable 3D Affordance Learning with Cross-Modal Consistency","created":"2024-12-12","cited_by_count":18,"area":null},{"id":"2503.16282","title":"Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model","created":"2025-03-20","cited_by_count":17,"area":"mllm"},{"id":"2507.13364","title":"OmniVec2 -- A Novel Transformer based Network for Large Scale Multimodal and Multitask Learning","created":"2025-07-06","cited_by_count":14,"area":null},{"id":"2210.06944","title":"SageMix: Saliency-Guided Mixup for Point Clouds","created":"2022-10-13","cited_by_count":12,"area":null},{"id":"2207.11554","title":"3DOS: Towards 3D Open Set Learning -- Benchmarking and Understanding Semantic Novelty Detection on Point Clouds","created":"2022-07-23","cited_by_count":11,"area":null},{"id":"2212.02011","title":"PointCaM: Cut-and-Mix for Open-Set Point Cloud Learning","created":"2022-12-05","cited_by_count":11,"area":null},{"id":"2204.03957","title":"Points to Patches: Enabling the Use of Self-Attention for 3D Shape Recognition","created":"2022-04-08","cited_by_count":9,"area":null},{"id":"2210.05896","title":"Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancement","created":"2022-10-12","cited_by_count":9,"area":null},{"id":"2109.00452","title":"Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes","created":"2021-09-01","cited_by_count":8,"area":null},{"id":"2204.08824","title":"Semi-supervised 3D shape segmentation with multilevel consistency and part substitution","created":"2022-04-19","cited_by_count":8,"area":null},{"id":"2107.00430","title":"Language-Level Semantics Conditioned 3D Point Cloud Segmentation","created":"2021-07-01","cited_by_count":7,"area":null},{"id":"2210.13428","title":"PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds","created":"2022-10-24","cited_by_count":7,"area":null},{"id":"2305.12853","title":"Real-Aug: Realistic Scene Synthesis for LiDAR Augmentation in 3D Object Detection","created":"2023-05-22","cited_by_count":7,"area":null},{"id":"2407.11011","title":"Toward Availability Attacks in 3D Point Clouds","created":"2024-06-26","cited_by_count":7,"area":null},{"id":"2309.10431","title":"Sample-adaptive Augmentation for Point Cloud Recognition Against Real-world Corruptions","created":"2023-09-19","cited_by_count":6,"area":null},{"id":"2310.03670","title":"Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning","created":"2023-09-25","cited_by_count":6,"area":null},{"id":"2403.07359","title":"FSC: Few-point Shape Completion","created":"2024-03-12","cited_by_count":6,"area":null},{"id":"2403.07706","title":"Fast and Simple Explainability for Point Cloud Networks","created":"2024-03-12","cited_by_count":6,"area":null},{"id":"2412.14974","title":"Arti-PG: A Toolbox for Procedurally Synthesizing Large-Scale and Diverse Articulated Objects with Rich Annotations","created":"2024-12-19","cited_by_count":6,"area":null},{"id":"2303.11419","title":"EPiC: Ensemble of Partial Point Clouds for Robust Classification","created":"2023-03-20","cited_by_count":5,"area":null},{"id":"2303.06678","title":"PointPatchMix: Point Cloud Mixing with Patch Scoring","created":"2023-03-12","cited_by_count":4,"area":null},{"id":"2103.15027","title":"Noise Injection-based Regularization for Point Cloud Processing","created":"2021-03-28","cited_by_count":3,"area":null},{"id":"2303.09219","title":"MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency","created":"2023-03-16","cited_by_count":3,"area":null},{"id":"2507.09102","title":"Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning","created":"2025-07-12","cited_by_count":3,"area":"diffusion"},{"id":"2305.10293","title":"Infinite Class Mixup","created":"2023-05-17","cited_by_count":2,"area":null},{"id":"2308.05525","title":"Robustifying Point Cloud Networks by Refocusing","created":"2023-08-10","cited_by_count":2,"area":null},{"id":"2405.18523","title":"MM-Mixing: Multi-Modal Mixing Alignment for 3D Understanding","created":"2024-05-28","cited_by_count":2,"area":null},{"id":"2408.11280","title":"Exploring Scene Affinity for Semi-Supervised LiDAR Semantic Segmentation","created":"2024-08-21","cited_by_count":2,"area":null},{"id":"2411.00462","title":"Target-Guided Adversarial Point Cloud Transformer Towards Recognition Against Real-world Corruptions","created":"2024-11-01","cited_by_count":2,"area":null},{"id":"2112.06029","title":"On Automatic Data Augmentation for 3D Point Cloud Classification","created":"2021-12-11","cited_by_count":1,"area":null},{"id":"2210.15514","title":"Point-Voxel Adaptive Feature Abstraction for Robust Point Cloud Classification","created":"2022-10-27","cited_by_count":1,"area":null},{"id":"2211.10880","title":"PartCom: Part Composition Learning for 3D Open-Set Recognition","created":"2022-11-20","cited_by_count":1,"area":null},{"id":"2310.03360","title":"CSI: Enhancing the Robustness of 3D Point Cloud Recognition against Corruption","created":"2023-10-05","cited_by_count":1,"area":null},{"id":"2311.06070","title":"Learning-Based Biharmonic Augmentation for Point Cloud Classification","created":"2023-11-10","cited_by_count":0,"area":null},{"id":"2407.10806","title":"Enhancing Robustness to Noise Corruption for Point Cloud Recognition via Spatial Sorting and Set-Mixing Aggregation Module","created":"2024-07-15","cited_by_count":0,"area":null},{"id":"2408.12062","title":"Enhancing Sampling Protocol for Point Cloud Classification Against Corruptions","created":"2024-08-22","cited_by_count":0,"area":null},{"id":"2408.13788","title":"3D-VirtFusion: Synthetic 3D Data Augmentation through Generative Diffusion Models and Controllable Editing","created":"2024-08-25","cited_by_count":0,"area":"diffusion"},{"id":"2506.13224","title":"SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point Clouds","created":"2025-06-16","cited_by_count":0,"area":null},{"id":"2509.22150","title":"Joint graph entropy knowledge distillation for point cloud classification and robustness against corruptions","created":"2025-09-26","cited_by_count":0,"area":"distill"},{"id":"2509.23010","title":"Desensitizing for Improving Corruption Robustness in Point Cloud Classification through Adversarial Training","created":"2025-09-27","cited_by_count":0,"area":null},{"id":"2602.05522","title":"Structure-First Point Cloud Learning with Mapper Region Graphs","created":"2026-02-05","cited_by_count":0,"area":null},{"id":"2605.07590","title":"Beyond Defenses: Manifold-Aligned Regularization for Intrinsic 3D Point Cloud Robustness","created":"2026-05-08","cited_by_count":0,"area":null}],"next_cursor":null}