point-cloud.md

Point Cloud

• (ICRA'21) NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation, [Paper]
• (arXiv 2020.12) Point Transformer, [Paper]
• (arXiv 2020.12) 3D Object Detection with Pointformer, [Paper]
• (arXiv 2020.12) PCT: Point Cloud Transformer, [Paper]
• (arXiv 2021.03) You Only Group Once: Efficient Point-Cloud Processing with Token Representation and Relation Inference Module, [Paper], [Code]
• (arXiv 2021.04) Group-Free 3D Object Detection via Transformers, [Paper], [Code]
• (arXiv 2021.04) M3DETR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers, [Paper]
• (arXiv 2021.04) Dual Transformer for Point Cloud Analysis, [Paper]
• (arXiv 2021.04) Point Cloud Learning with Transformer, [Paper]
• (arXiv 2021.08) SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer, [Paper], [Code]
• (arXiv 2021.08) PTT: Point-Track-Transformer Module for 3D Single Object Tracking in Point Clouds, [Paper], [Code]
• (arXiv 2021.08) Point-Voxel Transformer: An Efficient Approach To 3D Deep Learning, [Paper], [Code]
• (arXiv 2021.08) PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers, [Paper], [Code]
• (arXiv 2021.08) Improving 3D Object Detection with Channel-wise Transformer, [Paper], [Code]
• (arXiv 2021.09) PQ-Transformer: Jointly Parsing 3D Objects and Layouts from Point Clouds, [Paper], [Code]
• (arXiv 2021.09) An End-to-End Transformer Model for 3D Object Detection, [Paper]
• (arXiv 2021.10) Spatial-Temporal Transformer for 3D Point Cloud Sequences, [Paper]
• (arXiv 2021.10) PatchFormer: A Versatile 3D Transformer Based on Patch Attention, [Paper]
• (arXiv 2021.11) CpT: Convolutional Point Transformer for 3D Point Cloud Processing, [Paper]
• (arXiv 2021.11) PU-Transformer: Point Cloud Upsampling Transformer, [Paper]
• (arXiv 2021.11) Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling, [Paper], [Code]
• (arXiv 2021.11) Adaptive Channel Encoding Transformer for Point Cloud Analysis, [Paper], [Code]
• (arXiv 2021.11) Fast Point Transformer, [Paper]
• (arXiv 2021.12) Embracing Single Stride 3D Object Detector with Sparse Transformer, [Paper], [Code]
• (arXiv 2021.12) Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction, [Paper], [Code]
• (arXiv 2022.02) Geometric Transformer for Fast and Robust Point Cloud Registration, [Paper], [Code]
• (arXiv 2022.02) LighTN: Light-weight Transformer Network for Performance-overhead Tradeoff in Point Cloud Downsampling, [Paper]
• (arXiv 2022.02) PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Paths, [Paper]
• (arXiv 2022.02) Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformer, [Paper], [Code]
• (arXiv 2022.03) Spatiotemporal Transformer Attention Network for 3D Voxel Level Joint Segmentation and Motion Prediction in Point Cloud, [Paper]
• (arXiv 2022.03) 3DCTN: 3D Convolution-Transformer Network for Point Cloud Classification, [Paper]
• (arXiv 2022.03) Masked Autoencoders for Point Cloud Self-supervised Learning, [Paper]
• (arXiv 2022.03) CodedVTR: Codebook-based Sparse Voxel Transformer with Geometric Guidance, [Paper]
• (arXiv 2022.03) Masked Discrimination for Self-Supervised Learning on Point Clouds, [Paper], [Code]
• (arXiv 2022.03) Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds, [Paper], [Code]
• (arXiv 2022.03) V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer, [Paper]
• (arXiv 2022.03) REGTR: End-to-end Point Cloud Correspondences with Transformers, [Paper], [Code]
• (arXiv 2022.03) Stratified Transformer for 3D Point Cloud Segmentation, [Paper], [Code]
• (arXiv 2022.04) HiTPR: Hierarchical Transformer for Place Recognition in Point Cloud, [Paper]
• (arXiv 2022.04) Spatiality-guided Transformer for 3D Dense Captioning on Point Clouds, [Paper], [Code]
• (arXiv 2022.04) Panoptic-PHNet: Towards Real-Time and High-Precision LiDAR Panoptic Segmentation via Clustering Pseudo Heatmap, [Paper]
• (arXiv 2022.04) VNT-Net: Rotational Invariant Vector Neuron Transformers, [Paper]
• (arXiv 2022.05) Towards Model Generalization for Monocular 3D Object Detection, [Paper]
• (arXiv 2022.05) CompleteDT: Point Cloud Completion with Dense Augment Inference Transformers, [Paper]
• (arXiv 2022.05) TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving, [Paper], [Code]
• (arXiv 2022.06) SpikiLi: A Spiking Simulation of LiDAR based Real-time Object Detection for Autonomous Driving, [Paper]
• (arXiv 2022.06) VN-Transformer: Rotation-Equivariant Attention for Vector Neurons, [Paper]
• (arXiv 2022.06) PST: Plant Segmentation Transformer Enhanced Phenotyping of MLS Oilseed Rape Point Cloud, [Paper]
• (arXiv 2022.07) SeedFormer: Patch Seeds based Point Cloud Completion with Upsample Transformer, [Paper], [Code]
• (arXiv 2022.07) Geodesic-Former: a Geodesic-Guided Few-shot 3D Point Cloud Instance Segmenter, [Paper], [Code]
• (arXiv 2022.07) Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection from Point Clouds, [Paper]
• (arXiv 2022.08) Point Primitive Transformer for Long-Term 4D Point Cloud Video Understanding, [Paper]
• (arXiv 2022.08) Exploring Point-BEV Fusion for 3D Point Cloud Object Tracking with Transformer, [Paper], [Code]
• (arXiv 2022.08) PointTree: Transformation-Robust Point Cloud Encoder with Relaxed K-D Trees, [Paper], [Code]
• (arXiv 2022.08) Pix4Point: Image Pretrained Transformers for 3D Point Cloud Understanding, [Paper], [Code]
• (arXiv 2022.09) 3DPCT: 3D Point Cloud Transformer with Dual Self-attention, [Paper]
• (arXiv 2022.10) Transformers for Object Detection in Large Point Clouds, [Paper]
• (arXiv 2022.10) Bridged Transformer for Vision and Point Cloud 3D Object Detection, [Paper]
• (arXiv 2022.10) Introducing Vision Transformer for Alzheimer's Disease classification task with 3D input, [Paper]
• (arXiv 2022.10) Point Cloud Recognition with Position-to-Structure Attention Transformers, [Paper]
• (arXiv 2022.10) Point Transformer V2: Grouped Vector Attention and Partition-based Pooling, [Paper], [Code]
• (arXiv 2022.10) SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds, [Paper]
• (arXiv 2022.10) LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformers, [Paper]
• (arXiv 2022.10) PSFormer: Point Transformer for 3D Salient Object Detection, [Paper]
• (arXiv 2022.11) Hyperbolic Cosine Transformer for LiDAR 3D Object Detection, [Paper]
• (arXiv 2022.11) Completing point cloud from few points by Wasserstein GAN and Transformers, [Paper], [Code]
• (arXiv 2022.11) PVT3D: Point Voxel Transformers for Place Recognition from Sparse Lidar Scans, [Paper]
• (arXiv 2022.11) 3D Point Positional Encoding for Multi-Camera 3D Object Detection Transformers, [Paper]
• (arXiv 2023.01) AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformers, [Paper], [Code]
• (arXiv 2023.01) Text to Point Cloud Localization with Relation-Enhanced Transformer, [Paper]
• (arXiv 2023.01) SAT: Size-Aware Transformer for 3D Point Cloud ation, [Paper]
• (arXiv 2023.01) DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets, [Paper], [Code]
• (arXiv 2023.01) PTA-Det: Point Transformer Associating Point cloud and Image for 3D Object Detection, [Paper]
• (arXiv 2023.01) FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer, [Paper]
• (arXiv 2023.01) Slice Transformer and Self-supervised Learning for 6DoF Localization in 3D Point Cloud Maps, [Paper]
• (arXiv 2023.02) TR3D: Towards Real-Time Indoor 3D Object Detection, [Paper], [Code]
• (arXiv 2023.02) ProxyFormer: Proxy Alignment Assisted Point Cloud Completion with Missing Part Sensitive Transformer, [Paper], [Code]
• (arXiv 2023.03) Applying Plain Transformers to Real-World Point Clouds, [Paper]
• (arXiv 2023.03) BPT: Binary Point Cloud Transformer for Place Recognition, [Paper]
• (arXiv 2023.03) Improving the quality of dental crown using a Transformer-based method, [Paper], [Code]
• (arXiv 2023.03) Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space, [Paper], [Code]
• (arXiv 2023.03) Efficient Transformer-based 3D Object Detection with Dynamic Token Halting, [Paper]
• (arXiv 2023.03) Rotation-Invariant Transformer for Point Cloud Matching, [Paper]
• (arXiv 2023.03) Quality evaluation of point clouds: a novel no-reference approach using transformer-based architecture, [Paper]
• (arXiv 2023.03) Spherical Transformer for LiDAR-based 3D Recognition, [Paper], [Code]
• (arXiv 2023.03) Context-Aware Transformer for 3D Point Cloud Automatic Annotation, [Paper]
• (arXiv 2023.03) ViPFormer: Efficient Vision-and-Pointcloud Transformer for Unsupervised Pointcloud Understanding, [Paper], [Code]
• (arXiv 2023.03) StarNet: Style-Aware 3D Point Cloud Generation, [Paper]
• (arXiv 2023.03) Self-positioning Point-based Transformer for Point Cloud Understanding, [Paper], [Code]
• (arXiv 2023.04) APPT : Asymmetric Parallel Point Transformer for 3D Point Cloud Understanding, [Paper]
• (arXiv 2023.04) PointCAT: Cross-Attention Transformer for point cloud, [Paper]
• (arXiv 2023.04) Multi-scale Geometry-aware Transformer for 3D Point Cloud Classification, [Paper]
• (arXiv 2023.04) Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding, [Paper], [Code]
• (arXiv 2023.04) PCPNet: An Efficient and Semantic-Enhanced Transformer Network for Point Cloud Prediction, [Paper], [Code]
• (arXiv 2023.04) Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation, [Paper], [Code]
• (arXiv 2023.05) PU-EdgeFormer: Edge Transformer for Dense Prediction in Point Cloud Upsampling, [Paper], [Code]
• (arXiv 2023.05) Point Transformer For Coronary Artery Labeling, [Paper]
• (arXiv 2023.06) Collect-and-Distribute Transformer for 3D Point Cloud Analysis, [Paper], [Code]
• (arXiv 2023.06) Efficient 3D ation with Superpoint Transformer, [Paper], [Code]
• (arXiv 2023.06) A deep dive into explainable self-supervised transformers for point clouds, [Paper], [Code]
• (arXiv 2023.07) SVDFormer: Complementing Point Cloud via Self-view Augmentation and Self-structure Dual-generator, [Paper], [Code]
• (arXiv 2023.07) PSGformer: Enhancing 3D Point Cloud Instance Segmentation via Precise Semantic Guidance, [Paper]
• (arXiv 2023.07) PG-RCNN: Semantic Surface Point Generation for 3D Object Detection, [Paper], [Code
• (arXiv 2023.07) Two-stream Multi-level Dynamic Point Transformer for Two-person Interaction Recognition, [Paper]
• (arXiv 2023.07) pCTFusion: Point Convolution-Transformer Fusion with Semantic Aware Loss for Outdoor LiDAR Point Cloud Segmentation, [Paper]
• (arXiv 2023.08) Self-supervised Learning of Rotation-invariant 3D Point Set Features using Transformer and its Self-distillation, [Paper]
• (arXiv 2023.09) Weakly Supervised Point Clouds Transformer for 3D Object Detection, [Paper]
• (arXiv 2023.09) Research on self-cross transformer model of point cloud change detecter, [Paper]
• (arXiv 2023.09) Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds, [Paper]
• (arXiv 2023.10) Uni3D: Exploring Unified 3D Representation at Scale, [Paper], [Code]
• (arXiv 2023.10) 2D-3D Interlaced Transformer for Point Cloud Segmentation with Scene-Level Supervision, [Paper], [Code]
• (arXiv 2023.11) DeepEMD: A Transformer-based Fast Estimation of the Earth Mover鈥檚 Distance, [Paper], [Code]
• (arXiv 2023.11) OneFormer3D: One Transformer for Unified Point Cloud Segmentation, [Paper]
• (arXiv 2023.11) CalibFormer: A Transformer-based Automatic LiDAR-Camera Calibration Network, [Paper]
• (arXiv 2023.12) Fast Training of Diffusion Transformer with Extreme Masking for 3D Point Clouds Generation, [Paper], [Code]
• (arXiv 2023.12) TULIP: Transformer for Upsampling of LiDAR Point Cloud, [Paper]
• (arXiv 2023.12) PTT: Point-Trajectory Transformer for Efficient Temporal 3D Object Detection, [Paper], [Code]
• (arXiv 2023.12) Point Transformer V3: Simpler, Faster, Stronger, [Paper], [Code]
• (arXiv 2023.12) ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding, [Paper], [Code]
• (arXiv 2023.12) Group Multi-View Transformer for 3D Shape Analysis with Spatial Encoding, [Paper]
• (arXiv 2024.01) 3D Landmark Detection on Human Point Clouds: A Benchmark and A Dual Cascade Point Transformer Framework, [Paper]
• (arXiv 2024.01) CascadeV-Det: Cascade Point Voting for 3D Object Detection, [Paper]
• (arXiv 2024.01) Adaptive Point Transformer, [Paper]
• (arXiv 2024.02) AYDIV: Adaptable Yielding 3D Object Detection via Integrated Contextual Vision Transformer, [Paper]
• (arXiv 2024.02) DiffPoint: Single and Multi-view Point Cloud Reconstruction with ViT Based Diffusion Model, [Paper]
• (arXiv 2024.02) CAPT: Category-level Articulation Estimation from a Single Point Cloud Using Transformer, [Paper]
• (arXiv 2024.03) Region-Transformer: Self-Attention Region Based Class-Agnostic Point Cloud Segmentation, [Paper]
• (arXiv 2024.03) FBPT: A Fully Binary Point Transformer, [Paper]
• (arXiv 2024.03) Soft Masked Transformer for Point Cloud Processing with Skip Attention-Based Upsampling, [Paper]
• (arXiv 2024.03) DOCTR: Disentangled Object-Centric Transformer for Point Scene Understanding, [Paper], [Code]
• (arXiv 2024.04) Transfer Learning with Point Transformers, [Paper]
• (arXiv 2024.04) Taming Transformers for Realistic Lidar Point Cloud Generation, [Paper], [Code]
• (arXiv 2024.04) Sparse Points to Dense Clouds: Enhancing 3D Detection with Limited LiDAR Data, [Paper], [Code]
• (arXiv 2024.04) A Point-Based Approach to Efficient LiDAR Multi-Task Perception, [Paper]
• (arXiv 2024.05) 3D Learnable Supertoken Transformer for LiDAR Point Cloud Scene Segmentation, [Paper]
• (arXiv 2024.05) Efficient Point Transformer with Dynamic Token Aggregating for Point Cloud Processing, [Paper]
• (arXiv 2024.06) DMF-Net: Image-Guided Point Cloud Completion with Dual-Channel Modality Fusion and Shape-Aware Upsampling Transformer, [Paper]
• (arXiv 2024.07) GPSFormer: A Global Perception and Local Structure Fitting-based Transformer for Point Cloud Understanding, [Paper], [Code]
• (arXiv 2024.07) Adapt PointFormer: 3D Point Cloud Analysis via Adapting 2D Visual Transformers, [Paper], [Code]
• (arXiv 2024.07) TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds, [Paper]
• (arXiv 2024.07) SEED: A Simple and Effective 3D DETR in Point Clouds, [Paper], [Code]
• (arXiv 2024.07) Point Transformer V3 Extreme: 1st Place Solution for 2024 Waymo Open Dataset Challenge in Semantic Segmentation, [Paper], [Code]
• (arXiv 2024.07) Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers, [Paper], [Code]
• (arXiv 2024.07) Text2LiDAR: Text-guided LiDAR Point Cloud Generation via Equirectangular Transformer, [Paper], [Code]
• (arXiv 2024.08) Multi-view Hand Reconstruction with a Point-Embedded Transformer, [Paper], [Code]
• (arXiv 2024.09) ESP-PCT: Enhanced VR Semantic Performance through Efficient Compression of Temporal and Spatial Redundancies in Point Cloud Transformers, [Paper], [Code]
• (arXiv 2024.09) UniDet3D: Multi-dataset Indoor 3D Object Detection, [Paper], [Code]
• (arXiv 2024.09) HydroVision: LiDAR-Guided Hydrometric Prediction with Vision Transformers and Hybrid Graph Learning, [Paper]
• (arXiv 2024.11) Rendering-Oriented 3D Point Cloud Attribute Compression using Sparse Tensor-based Transformer, [Paper]
• (arXiv 2024.12) SP2T: Sparse Proxy Attention for Dual-stream Point Transformer, [Paper], [Code]
• (arXiv 2024.12) Flash3D: Super-scaling Point Transformers through Joint Hardware-Geometry Locality, [Paper]
• (arXiv 2024.12) DAPoinTr: Domain Adaptive Point Transformer for Point Cloud Completion, [Paper], [Code]
• (arXiv 2025.04) Uni4D: A Unified Self-Supervised Learning Framework for Point Cloud Videos, [Paper]