{"total":162,"rows":[{"id":"2210.05666","title":"Point Transformer V2: Grouped Vector Attention and Partition-based Pooling","created":"2022-10-11","cited_by_count":388,"area":null},{"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":"2303.06042","title":"MVImgNet: A Large-scale Dataset of Multi-view Images","created":"2023-03-10","cited_by_count":220,"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":"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":"2205.05740","title":"Surface Representation for Point Clouds","created":"2022-05-11","cited_by_count":84,"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":"2211.12046","title":"DP-NeRF: Deblurred Neural Radiance Field with Physical Scene Priors","created":"2022-11-22","cited_by_count":70,"area":null},{"id":"2209.13232","title":"A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective","created":"2022-09-27","cited_by_count":56,"area":null},{"id":"2203.04041","title":"Shape-invariant 3D Adversarial Point Clouds","created":"2022-03-08","cited_by_count":50,"area":null},{"id":"2111.00207","title":"PatchFormer: An Efficient Point Transformer with Patch Attention","created":"2021-10-30","cited_by_count":43,"area":null},{"id":"2304.11846","title":"Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions","created":"2023-04-24","cited_by_count":43,"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":"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":"2205.07417","title":"Transformers in 3D Point Clouds: A Survey","created":"2022-05-16","cited_by_count":31,"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":"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":"2311.02608","title":"Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlook","created":"2023-11-05","cited_by_count":24,"area":null},{"id":"2112.04863","title":"3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis","created":"2021-12-09","cited_by_count":23,"area":null},{"id":"2205.13412","title":"Physical-World Optical Adversarial Attacks on 3D Face Recognition","created":"2022-05-26","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":"2310.18511","title":"3DCoMPaT$^{++}$: An improved Large-scale 3D Vision Dataset for Compositional Recognition","created":"2023-10-27","cited_by_count":21,"area":null},{"id":"2210.03974","title":"FBNet: Feedback Network for Point Cloud Completion","created":"2022-10-08","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":"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":"2401.05906","title":"PartSTAD: 2D-to-3D Part Segmentation Task Adaptation","created":"2024-01-11","cited_by_count":18,"area":null},{"id":"2206.05398","title":"E2PN: Efficient SE(3)-Equivariant Point Network","created":"2022-06-11","cited_by_count":17,"area":null},{"id":"2203.09258","title":"Explainability in Graph Neural Networks: An Experimental Survey","created":"2022-03-17","cited_by_count":16,"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":"2208.09801","title":"PointDP: Diffusion-driven Purification against Adversarial Attacks on 3D Point Cloud Recognition","created":"2022-08-21","cited_by_count":16,"area":null},{"id":"2312.11112","title":"ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding","created":"2023-12-18","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":"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":"2202.10890","title":"HiP: Hierarchical Perceiver","created":"2022-02-22","cited_by_count":15,"area":null},{"id":"2403.15173","title":"LSK3DNet: Towards Effective and Efficient 3D Perception with Large Sparse Kernels","created":"2024-03-22","cited_by_count":15,"area":null},{"id":"2403.18442","title":"Backpropagation-free Network for 3D Test-time Adaptation","created":"2024-03-27","cited_by_count":14,"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":"2212.04761","title":"Leveraging Spatio-Temporal Dependency for Skeleton-Based Action Recognition","created":"2022-12-09","cited_by_count":13,"area":null},{"id":"2212.09948","title":"MM-3DScene: 3D Scene Understanding by Customizing Masked Modeling with Informative-Preserved Reconstruction and Self-Distilled Consistency","created":"2022-12-20","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":"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":"2307.09000","title":"TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representation","created":"2023-07-18","cited_by_count":11,"area":null},{"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":"2201.12733","title":"TPC: Transformation-Specific Smoothing for Point Cloud Models","created":"2022-01-30","cited_by_count":10,"area":null},{"id":"2204.05687","title":"3DeformRS: Certifying Spatial Deformations on Point Clouds","created":"2022-04-12","cited_by_count":10,"area":null},{"id":"2207.03128","title":"PointMCD: Boosting Deep Point Cloud Encoders via Multi-view Cross-modal Distillation for 3D Shape Recognition","created":"2022-07-07","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":"2403.17410","title":"On permutation-invariant neural networks","created":"2024-03-26","cited_by_count":10,"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":"2211.06841","title":"Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud Learning","created":"2022-11-13","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":"2411.10492","title":"MFP3D: Monocular Food Portion Estimation Leveraging 3D Point Clouds","created":"2024-11-14","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":"2205.15217","title":"GraphWalks: Efficient Shape Agnostic Geodesic Shortest Path Estimation","created":"2022-05-30","cited_by_count":8,"area":null},{"id":"2211.16247","title":"Ada3Diff: Defending against 3D Adversarial Point Clouds via Adaptive Diffusion","created":"2022-11-29","cited_by_count":8,"area":null},{"id":"2303.14376","title":"ViPFormer: Efficient Vision-and-Pointcloud Transformer for Unsupervised Pointcloud Understanding","created":"2023-03-25","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":"2212.14197","title":"PointVST: Self-Supervised Pre-training for 3D Point Clouds via View-Specific Point-to-Image Translation","created":"2022-12-29","cited_by_count":7,"area":null},{"id":"2302.02535","title":"PaRot: Patch-Wise Rotation-Invariant Network via Feature Disentanglement and Pose Restoration","created":"2023-02-06","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":"2403.01129","title":"Dynamic 3D Point Cloud Sequences as 2D Videos","created":"2024-03-02","cited_by_count":7,"area":null},{"id":"2111.14658","title":"diffConv: Analyzing Irregular Point Clouds with an Irregular View","created":"2021-11-29","cited_by_count":6,"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":"2303.01959","title":"PointCert: Point Cloud Classification with Deterministic Certified Robustness Guarantees","created":"2023-03-03","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":"2403.07706","title":"Fast and Simple Explainability for Point Cloud Networks","created":"2024-03-12","cited_by_count":6,"area":null},{"id":"2403.11113","title":"Local-consistent Transformation Learning for Rotation-invariant Point Cloud Analysis","created":"2024-03-17","cited_by_count":6,"area":null},{"id":"2404.01568","title":"A Linear Time and Space Local Point Cloud Geometry Encoder via Vectorized Kernel Mixture (VecKM)","created":"2024-04-02","cited_by_count":6,"area":null},{"id":"2410.20406","title":"Point-PRC: A Prompt Learning Based Regulation Framework for Generalizable Point Cloud Analysis","created":"2024-10-27","cited_by_count":6,"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":"2303.12050","title":"CurveCloudNet: Processing Point Clouds with 1D Structure","created":"2023-03-21","cited_by_count":5,"area":null},{"id":"2307.00309","title":"Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey","created":"2023-07-01","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":"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":"2112.01050","title":"CloudWalker: Random walks for 3D point cloud shape analysis","created":"2021-12-02","cited_by_count":4,"area":null},{"id":"2203.00172","title":"Enhancing Local Feature Learning for 3D Point Cloud Processing using Unary-Pairwise Attention","created":"2022-03-01","cited_by_count":4,"area":null},{"id":"2301.10531","title":"3D Tooth Mesh Segmentation with Simplified Mesh Cell Representation","created":"2023-01-25","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":"2410.16317","title":"A Survey on Physical Adversarial Attacks against Face Recognition Systems","created":"2024-10-10","cited_by_count":4,"area":null},{"id":"2501.06785","title":"3DCoMPaT200: Language-Grounded Compositional Understanding of Parts and Materials of 3D Shapes","created":"2025-01-12","cited_by_count":4,"area":null},{"id":"2503.10055","title":"Fourier Decomposition for Explicit Representation of 3D Point Cloud Attributes","created":"2025-03-13","cited_by_count":4,"area":null},{"id":"2503.12150","title":"Point-Cache: Test-time Dynamic and Hierarchical Cache for Robust and Generalizable Point Cloud Analysis","created":"2025-03-15","cited_by_count":4,"area":null},{"id":"2205.01006","title":"Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding","created":"2022-05-02","cited_by_count":3,"area":null},{"id":"2207.06324","title":"PointNorm: Dual Normalization is All You Need for Point Cloud Analysis","created":"2022-07-13","cited_by_count":3,"area":null},{"id":"2210.04240","title":"Less is More: Facial Landmarks can Recognize a Spontaneous Smile","created":"2022-10-09","cited_by_count":3,"area":null},{"id":"2303.10543","title":"GAM : Gradient Attention Module of Optimization for Point Clouds Analysis","created":"2023-03-19","cited_by_count":3,"area":null},{"id":"2306.10798","title":"ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformers","created":"2023-06-19","cited_by_count":3,"area":null},{"id":"2310.07743","title":"PointHR: Exploring High-Resolution Architectures for 3D Point Cloud Segmentation","created":"2023-10-11","cited_by_count":3,"area":null},{"id":"2408.05508","title":"PointMT: Efficient Point Cloud Analysis with Hybrid MLP-Transformer Architecture","created":"2024-08-10","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":"2501.14238","title":"Point-LN: A Lightweight Framework for Efficient Point Cloud Classification Using Non-Parametric Positional Encoding","created":"2025-01-24","cited_by_count":3,"area":null},{"id":"2509.05198","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","created":"2025-09-05","cited_by_count":3,"area":null},{"id":"2211.10580","title":"Normal Transformer: Extracting Surface Geometry from LiDAR Points Enhanced by Visual Semantics","created":"2022-11-19","cited_by_count":2,"area":null},{"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},{"id":"2306.05553","title":"Equivariant vs. Invariant Layers: A Comparison of Backbone and Pooling for Point Cloud Classification","created":"2023-06-08","cited_by_count":2,"area":null}],"next_cursor":"100"}