{"total":145,"rows":[{"id":"2112.02413","title":"PointCLIP: Point Cloud Understanding by CLIP","created":"2021-12-04","cited_by_count":463,"area":"mllm"},{"id":"2206.04670","title":"PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies","created":"2022-06-09","cited_by_count":436,"area":"wm"},{"id":"2202.07123","title":"Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework","created":"2022-02-15","cited_by_count":379,"area":null},{"id":"2301.07525","title":"OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation","created":"2023-01-18","cited_by_count":326,"area":null},{"id":"2212.05171","title":"ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding","created":"2022-12-10","cited_by_count":267,"area":null},{"id":"2306.14896","title":"RVT: Robotic View Transformer for 3D Object Manipulation","created":"2023-06-26","cited_by_count":225,"area":null},{"id":"2211.11682","title":"PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning","created":"2022-11-21","cited_by_count":187,"area":null},{"id":"2302.02318","title":"Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining","created":"2023-02-05","cited_by_count":164,"area":null},{"id":"2210.01055","title":"CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training","created":"2022-10-03","cited_by_count":160,"area":null},{"id":"2212.06785","title":"Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders","created":"2022-12-13","cited_by_count":144,"area":null},{"id":"2406.08545","title":"RVT-2: Learning Precise Manipulation from Few Demonstrations","created":"2024-06-12","cited_by_count":137,"area":null},{"id":"2212.08320","title":"Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?","created":"2022-12-16","cited_by_count":122,"area":"distill"},{"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":"2202.03377","title":"Benchmarking and Analyzing Point Cloud Classification under Corruptions","created":"2022-02-07","cited_by_count":70,"area":null},{"id":"2303.16894","title":"ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding with GPT and Prototype Guidance","created":"2023-03-29","cited_by_count":70,"area":null},{"id":"2304.07221","title":"Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models","created":"2023-04-14","cited_by_count":68,"area":null},{"id":"2211.10598","title":"LidarGait: Benchmarking 3D Gait Recognition with Point Clouds","created":"2022-11-19","cited_by_count":55,"area":null},{"id":"2302.14007","title":"Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training","created":"2023-02-27","cited_by_count":52,"area":null},{"id":"2112.01473","title":"Neural Point Light Fields","created":"2021-12-02","cited_by_count":51,"area":null},{"id":"2203.04041","title":"Shape-invariant 3D Adversarial Point Clouds","created":"2022-03-08","cited_by_count":50,"area":null},{"id":"2206.13732","title":"A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges","created":"2022-06-28","cited_by_count":45,"area":null},{"id":"2312.10726","title":"Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders","created":"2023-12-17","cited_by_count":42,"area":null},{"id":"2303.16450","title":"Self-positioning Point-based Transformer for Point Cloud Understanding","created":"2023-03-29","cited_by_count":40,"area":null},{"id":"2310.12973","title":"Frozen Transformers in Language Models Are Effective Visual Encoder Layers","created":"2023-10-19","cited_by_count":39,"area":null},{"id":"2303.08134","title":"Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis","created":"2023-03-14","cited_by_count":34,"area":null},{"id":"2410.00320","title":"PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection","created":"2024-10-01","cited_by_count":29,"area":null},{"id":"2305.13456","title":"Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matters","created":"2023-05-22","cited_by_count":27,"area":null},{"id":"2210.15291","title":"Isometric 3D Adversarial Examples in the Physical World","created":"2022-10-27","cited_by_count":25,"area":null},{"id":"2307.14971","title":"Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud Models","created":"2023-07-27","cited_by_count":25,"area":null},{"id":"2210.04208","title":"Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysis","created":"2022-10-09","cited_by_count":24,"area":null},{"id":"2408.08753","title":"PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders","created":"2024-08-16","cited_by_count":24,"area":null},{"id":"2211.11432","title":"MATE: Masked Autoencoders are Online 3D Test-Time Learners","created":"2022-11-21","cited_by_count":23,"area":null},{"id":"2111.15363","title":"Voint Cloud: Multi-View Point Cloud Representation for 3D Understanding","created":"2021-11-30","cited_by_count":22,"area":null},{"id":"2205.10528","title":"PointVector: A Vector Representation In Point Cloud Analysis","created":"2022-05-21","cited_by_count":21,"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":"2209.10318","title":"Rethinking the compositionality of point clouds through regularization in the hyperbolic space","created":"2022-09-21","cited_by_count":18,"area":null},{"id":"2111.10332","title":"DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion","created":"2021-11-19","cited_by_count":17,"area":null},{"id":"2206.11925","title":"Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets","created":"2022-06-23","cited_by_count":17,"area":null},{"id":"2204.00989","title":"POS-BERT: Point Cloud One-Stage BERT Pre-Training","created":"2022-04-03","cited_by_count":16,"area":null},{"id":"2402.15726","title":"CLIPose: Category-Level Object Pose Estimation with Pre-trained Vision-Language Knowledge","created":"2024-02-24","cited_by_count":16,"area":null},{"id":"2402.15823","title":"Parameter-efficient Prompt Learning for 3D Point Cloud Understanding","created":"2024-02-24","cited_by_count":16,"area":null},{"id":"2405.13194","title":"KPConvX: Modernizing Kernel Point Convolution with Kernel Attention","created":"2024-05-21","cited_by_count":16,"area":null},{"id":"2407.05862","title":"Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learning","created":"2024-07-08","cited_by_count":16,"area":null},{"id":"2410.03644","title":"Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need","created":"2024-10-04","cited_by_count":16,"area":null},{"id":"2207.03111","title":"Masked Surfel Prediction for Self-Supervised Point Cloud Learning","created":"2022-07-07","cited_by_count":15,"area":null},{"id":"2302.09997","title":"A Large Scale Homography Benchmark","created":"2023-02-20","cited_by_count":15,"area":null},{"id":"2412.04247","title":"3D Part Segmentation via Geometric Aggregation of 2D Visual Features","created":"2024-12-05","cited_by_count":15,"area":null},{"id":"2206.04665","title":"AGConv: Adaptive Graph Convolution on 3D Point Clouds","created":"2022-06-09","cited_by_count":13,"area":null},{"id":"2308.09694","title":"Invariant Training 2D-3D Joint Hard Samples for Few-Shot Point Cloud Recognition","created":"2023-08-18","cited_by_count":13,"area":null},{"id":"2111.03549","title":"Interpreting Representation Quality of DNNs for 3D Point Cloud Processing","created":"2021-11-05","cited_by_count":12,"area":null},{"id":"2308.16532","title":"Decoupled Local Aggregation for Point Cloud Learning","created":"2023-08-31","cited_by_count":12,"area":null},{"id":"2407.13519","title":"GPSFormer: A Global Perception and Local Structure Fitting-based Transformer for Point Cloud Understanding","created":"2024-07-18","cited_by_count":12,"area":null},{"id":"2408.06507","title":"Benchmarking tree species classification from proximally-sensed laser scanning data: introducing the FOR-species20K dataset","created":"2024-08-12","cited_by_count":12,"area":null},{"id":"2410.00871","title":"MAP: Unleashing Hybrid Mamba-Transformer Vision Backbone's Potential with Masked Autoregressive Pretraining","created":"2024-10-01","cited_by_count":12,"area":"ssm"},{"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":"2311.18402","title":"MV-CLIP: Multi-View CLIP for Zero-shot 3D Shape Recognition","created":"2023-11-30","cited_by_count":11,"area":null},{"id":"2407.02038","title":"Camera-LiDAR Cross-modality Gait Recognition","created":"2024-07-02","cited_by_count":11,"area":null},{"id":"2512.08330","title":"PointDico: Contrastive 3D Representation Learning Guided by Diffusion Models","created":"2025-12-09","cited_by_count":11,"area":"diffusion"},{"id":"2111.08799","title":"DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds","created":"2021-11-16","cited_by_count":10,"area":null},{"id":"2208.08580","title":"MvDeCor: Multi-view Dense Correspondence Learning for Fine-grained 3D Segmentation","created":"2022-08-18","cited_by_count":10,"area":null},{"id":"2209.07788","title":"PointCAT: Contrastive Adversarial Training for Robust Point Cloud Recognition","created":"2022-09-16","cited_by_count":10,"area":null},{"id":"2303.06346","title":"3DInAction: Understanding Human Actions in 3D Point Clouds","created":"2023-03-11","cited_by_count":10,"area":null},{"id":"2108.06317","title":"Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification","created":"2021-08-13","cited_by_count":9,"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":"2205.09962","title":"Advanced Feature Learning on Point Clouds using Multi-resolution Features and Learnable Pooling","created":"2022-05-20","cited_by_count":9,"area":null},{"id":"2308.07546","title":"3DHacker: Spectrum-based Decision Boundary Generation for Hard-label 3D Point Cloud Attack","created":"2023-08-15","cited_by_count":9,"area":null},{"id":"2403.06403","title":"PointSeg: A Training-Free Paradigm for 3D Scene Segmentation via Foundation Models","created":"2024-03-11","cited_by_count":9,"area":null},{"id":"2110.11036","title":"RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation","created":"2021-10-21","cited_by_count":8,"area":null},{"id":"2412.03056","title":"Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud Classification","created":"2024-12-04","cited_by_count":8,"area":null},{"id":"2212.13462","title":"MVTN: Learning Multi-View Transformations for 3D Understanding","created":"2022-12-27","cited_by_count":7,"area":null},{"id":"2302.05201","title":"PointWavelet: Learning in Spectral Domain for 3D Point Cloud Analysis","created":"2023-02-10","cited_by_count":7,"area":null},{"id":"2509.01250","title":"Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views","created":"2025-09-01","cited_by_count":7,"area":null},{"id":"2203.16001","title":"Meta-Sampler: Almost-Universal yet Task-Oriented Sampling for Point Clouds","created":"2022-03-30","cited_by_count":6,"area":null},{"id":"2208.12259","title":"Pix4Point: Image Pretrained Standard Transformers for 3D Point Cloud Understanding","created":"2022-08-25","cited_by_count":6,"area":null},{"id":"2307.10875","title":"Risk-optimized Outlier Removal for Robust 3D Point Cloud Classification","created":"2023-07-20","cited_by_count":6,"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":"2312.13071","title":"Point Deformable Network with Enhanced Normal Embedding for Point Cloud Analysis","created":"2023-12-20","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":"2504.14337","title":"Multispectral airborne laser scanning for tree species classification: a benchmark of machine learning and deep learning algorithms","created":"2025-04-19","cited_by_count":6,"area":null},{"id":"2201.03454","title":"3D Face Morphing Attacks: Generation, Vulnerability and Detection","created":"2022-01-10","cited_by_count":5,"area":null},{"id":"2203.03833","title":"Quasi-Balanced Self-Training on Noise-Aware Synthesis of Object Point Clouds for Closing Domain Gap","created":"2022-03-08","cited_by_count":5,"area":null},{"id":"2303.00703","title":"Nearest Neighbors Meet Deep Neural Networks for Point Cloud Analysis","created":"2023-03-01","cited_by_count":5,"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":"2305.03815","title":"Persistent Homology Meets Object Unity: Object Recognition in Clutter","created":"2023-05-05","cited_by_count":5,"area":null},{"id":"2401.07572","title":"Exploiting GPT-4 Vision for Zero-shot Point Cloud Understanding","created":"2024-01-15","cited_by_count":5,"area":null},{"id":"2401.11650","title":"PointGL: A Simple Global-Local Framework for Efficient Point Cloud Analysis","created":"2024-01-22","cited_by_count":5,"area":null},{"id":"2512.22991","title":"Fusion or Confusion? Multimodal Complexity Is Not All You Need","created":"2025-12-28","cited_by_count":5,"area":null},{"id":"2109.01291","title":"LATFormer: Locality-Aware Point-View Fusion Transformer for 3D Shape Recognition","created":"2021-09-03","cited_by_count":4,"area":null},{"id":"2205.00847","title":"APP-Net: Auxiliary-point-based Push and Pull Operations for Efficient Point Cloud Classification","created":"2022-05-02","cited_by_count":4,"area":null},{"id":"2303.06678","title":"PointPatchMix: Point Cloud Mixing with Patch Scoring","created":"2023-03-12","cited_by_count":4,"area":null},{"id":"2306.01257","title":"Collect-and-Distribute Transformer for 3D Point Cloud Analysis","created":"2023-06-02","cited_by_count":4,"area":null},{"id":"2409.08388","title":"Continual Learning in 3D Point Clouds: Employing Spectral Techniques for Exemplar Selection","created":"2024-09-12","cited_by_count":4,"area":null},{"id":"2211.15759","title":"PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud","created":"2022-11-28","cited_by_count":3,"area":null},{"id":"2401.08210","title":"ModelNet-O: A Large-Scale Synthetic Dataset for Occlusion-Aware Point Cloud Classification","created":"2024-01-16","cited_by_count":3,"area":null},{"id":"2408.14601","title":"3D Point Cloud Network Pruning: When Some Weights Do not Matter","created":"2024-08-26","cited_by_count":3,"area":null},{"id":"2410.06418","title":"MIRACLE3D: Memory-efficient Integrated Robust Approach for Continual Learning on Point Clouds via Shape Model Construction","created":"2024-10-08","cited_by_count":3,"area":null},{"id":"2504.13593","title":"KAN or MLP? Point Cloud Shows the Way Forward","created":"2025-04-18","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":"2211.14456","title":"TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis","created":"2022-11-26","cited_by_count":2,"area":null}],"next_cursor":"100"}