Trending repositories for topic point-cloud
Official PyTorch implementation of Superpoint Transformer introduced in [ICCV'23] "Efficient 3D Semantic Segmentation with Superpoint Transformer" and SuperCluster introduced in [3DV'24 Oral] "Scalabl...
OpenMMLab's next-generation platform for general 3D object detection.
PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
Source code for the article "GroundGrid: LiDAR Point Cloud Ground Segmentation and Terrain Estimation"
Pointcept: a codebase for point cloud perception research. Latest works: PTv3 (CVPR'24 Oral), PPT (CVPR'24), OA-CNNs (CVPR'24), MSC (CVPR'23)
User-friendly, commercial-grade software for processing aerial imagery. 🛩
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
This repository is used for automatic calibration between high resolution LiDAR and camera in targetless scenes.
:books: The list of vision-based SLAM / Visual Odometry open source, blogs, and papers
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
[ICRA 2022] CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation
Baseline model for "GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping" (CVPR 2020)
From anything to mesh like human artists. Official impl. of "MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers"
Specification for streaming massive heterogeneous 3D geospatial datasets :earth_americas:
3DGS-to-PC: Convert a 3D gaussian splatting scene into a dense point cloud with advanced customisation options and high-accuracy rendered point colours
Source code for the article "GroundGrid: LiDAR Point Cloud Ground Segmentation and Terrain Estimation"
Autoware 安装运行应用中文教程指南，包含部分关键代码注释。Manuals & Tutorials for Autoware in Chinese.
Code for "Cross-modal Learning for Image-Guided Point Cloud Shape Completion" (NeurIPS 2022)
Official PyTorch implementation of Superpoint Transformer introduced in [ICCV'23] "Efficient 3D Semantic Segmentation with Superpoint Transformer" and SuperCluster introduced in [3DV'24 Oral] "Scalabl...
Reconstructing compact building models from point clouds using deep implicit fields [ISPRS 2022]
Code Accompany NeurIPS 2022 Dataset Paper: Breaking Bad. A Shape Assembly Benchmark Implemented in PyTorch
A graphical user interface to annotate point clouds and 3D data.
[ICRA 2022] CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation
[ICCV2023] Official Implementation of "UniTR: A Unified and Efficient Multi-Modal Transformer for Bird’s-Eye-View Representation"
Baseline model for "GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping" (CVPR 2020)
[CVPR 2022 Oral] SoftGroup for Instance Segmentation on 3D Point Clouds
This repository is used for automatic calibration between high resolution LiDAR and camera in targetless scenes.
[CVPR2023] Official Implementation of "DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets"
A collection of GTSAM factors and optimizers for point cloud SLAM
Nexus is a c++/javascript library for creation and visualization of a batched multiresolution mesh
[ECCV2022] Masked Autoencoders for Point Cloud Self-supervised Learning
Pointcept: a codebase for point cloud perception research. Latest works: PTv3 (CVPR'24 Oral), PPT (CVPR'24), OA-CNNs (CVPR'24), MSC (CVPR'23)
Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions (RAL 2022)
OpenMMLab's next-generation platform for general 3D object detection.
Official PyTorch implementation of Superpoint Transformer introduced in [ICCV'23] "Efficient 3D Semantic Segmentation with Superpoint Transformer" and SuperCluster introduced in [3DV'24 Oral] "Scalabl...
Pointcept: a codebase for point cloud perception research. Latest works: PTv3 (CVPR'24 Oral), PPT (CVPR'24), OA-CNNs (CVPR'24), MSC (CVPR'23)
PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
User-friendly, commercial-grade software for processing aerial imagery. 🛩
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Source code for the article "GroundGrid: LiDAR Point Cloud Ground Segmentation and Terrain Estimation"
A command line toolkit to generate maps, point clouds, 3D models and DEMs from drone, balloon or kite images. 📷
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Draco is a library for compressing and decompressing 3D geometric meshes and point clouds. It is intended to improve the storage and transmission of 3D graphics.
Photogrammetry Guide. Photogrammetry is widely used for Aerial surveying, Agriculture, Architecture, 3D Games, Robotics, Archaeology, Construction, Emergency management, and Medical.
[ECCV 2024 Best Paper Candidate] PointLLM: Empowering Large Language Models to Understand Point Clouds
Baseline model for "GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping" (CVPR 2020)
3DGS-to-PC: Convert a 3D gaussian splatting scene into a dense point cloud with advanced customisation options and high-accuracy rendered point colours
A cuda implementation of GPU Poisson Reconstruction.(Data-Parallel Octrees based)
Source code for the article "GroundGrid: LiDAR Point Cloud Ground Segmentation and Terrain Estimation"
[RA-L 2023] Official Repository of "Spectral Geometric Verification: Re-Ranking Point Cloud Retrieval for Metric Localization", RA-L, Volume 8, Issue 5, May 2023
Autoware 安装运行应用中文教程指南，包含部分关键代码注释。Manuals & Tutorials for Autoware in Chinese.
Combining Patches and Point Convolutions for Detailed Surface Reconstruction
Official PyTorch implementation of Superpoint Transformer introduced in [ICCV'23] "Efficient 3D Semantic Segmentation with Superpoint Transformer" and SuperCluster introduced in [3DV'24 Oral] "Scalabl...
Code for "Cross-modal Learning for Image-Guided Point Cloud Shape Completion" (NeurIPS 2022)
"Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis" (ECCV 2024)
[MICCAI'21 & TMI'23] RibSeg Dataset and Point Cloud Baselines for Rib Segmentation from CT Scans
Reconstructing compact building models from point clouds using deep implicit fields [ISPRS 2022]
Code Accompany NeurIPS 2022 Dataset Paper: Breaking Bad. A Shape Assembly Benchmark Implemented in PyTorch
[CVPR 2021] PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds
Self-Supervised Representation Learning on Point Clouds (GCPR 2023 | T4V Workshop @ CVPR 2023)
A graphical user interface to annotate point clouds and 3D data.
Repository for automatic classification and labeling of Urban PointClouds using data fusion and region growing techniques.
Baseline model for "GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping" (CVPR 2020)
[ICCV2023] Official Implementation of "UniTR: A Unified and Efficient Multi-Modal Transformer for Bird’s-Eye-View Representation"
PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
Pointcept: a codebase for point cloud perception research. Latest works: PTv3 (CVPR'24 Oral), PPT (CVPR'24), OA-CNNs (CVPR'24), MSC (CVPR'23)
OpenMMLab's next-generation platform for general 3D object detection.
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
[ECCV 2024 Best Paper Candidate] PointLLM: Empowering Large Language Models to Understand Point Clouds
Tooling for professional robotic development in C++ and Python with a touch of ROS, autonomous driving and aerospace.
Official PyTorch implementation of Superpoint Transformer introduced in [ICCV'23] "Efficient 3D Semantic Segmentation with Superpoint Transformer" and SuperCluster introduced in [3DV'24 Oral] "Scalabl...
User-friendly, commercial-grade software for processing aerial imagery. 🛩
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Draco is a library for compressing and decompressing 3D geometric meshes and point clouds. It is intended to improve the storage and transmission of 3D graphics.
A command line toolkit to generate maps, point clouds, 3D models and DEMs from drone, balloon or kite images. 📷
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
3DGS-to-PC: Convert a 3D gaussian splatting scene into a dense point cloud with advanced customisation options and high-accuracy rendered point colours
[NeurIPS 2024 D&B] Point Cloud Matters: Rethinking the Impact of Different Observation Spaces on Robot Learning
Autoware 安装运行应用中文教程指南，包含部分关键代码注释。Manuals & Tutorials for Autoware in Chinese.
Source code for the article "GroundGrid: LiDAR Point Cloud Ground Segmentation and Terrain Estimation"
Radar4Motion: 4D Imaging Radar based IMU-free Odometry with Radar Cross Section (RCS) weighted Correspondences
A collection of GTSAM factors and optimizers for point cloud SLAM
[RA-L 2023] Official Repository of "Spectral Geometric Verification: Re-Ranking Point Cloud Retrieval for Metric Localization", RA-L, Volume 8, Issue 5, May 2023
🔥(ECCV 2024 Oral) RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentation
[ECCV 2024 Best Paper Candidate] PointLLM: Empowering Large Language Models to Understand Point Clouds
Official PyTorch implementation of Superpoint Transformer introduced in [ICCV'23] "Efficient 3D Semantic Segmentation with Superpoint Transformer" and SuperCluster introduced in [3DV'24 Oral] "Scalabl...
Combining Patches and Point Convolutions for Detailed Surface Reconstruction
Code Accompany NeurIPS 2022 Dataset Paper: Breaking Bad. A Shape Assembly Benchmark Implemented in PyTorch
Cross platform 3D renderer in C++ and OpenGL with lighting, shadows and materials.
[Under Review] Efficient and Generic Point Model for Lossless Point Cloud Attribute Compression
A cuda implementation of GPU Poisson Reconstruction.(Data-Parallel Octrees based)
"Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis" (ECCV 2024)
Using the KITTI dataset, we employed Open3D to visualize, downsample, segment with RANSAC, cluster via DBSCAN, create 3D bounding boxes, and perform surface reconstruction on point clouds.
From anything to mesh like human artists. Official impl. of "MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers"
"Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis" (ECCV 2024)
[CVPR 2024] Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysis
💫 [CVPR 2024] LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis
A curated list of Place Recognition methods, datasets, and various algorithms for LiDAR
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
[CVPR 2024, highlight] Living Scenes: Multi-object Relocalization and Reconstruction in Changing 3D Environments
[MM 2024] [Need only a 3090] MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors
[ECCV'24] SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving
HeLiMOS: Heterogeneous LiDAR Dataset for Moving Object Segmentation @ IROS'2024
3DGS-to-PC: Convert a 3D gaussian splatting scene into a dense point cloud with advanced customisation options and high-accuracy rendered point colours
[NeurIPS 2024 D&B] Point Cloud Matters: Rethinking the Impact of Different Observation Spaces on Robot Learning
Using PyTorch's MiDaS model and Open3D's point cloud to map a scene in 3D 🏞️🔭
🔥(ECCV 2024 Oral) RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentation
3D DATA PROCESSING AND MODELING ESSENTIALS: A curated collection of tools, literature, courses, and datasets for mastering 3D point cloud technology.
[Under Review] Efficient and Generic Point Model for Lossless Point Cloud Attribute Compression
From anything to mesh like human artists. Official impl. of "MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers"
OpenMMLab's next-generation platform for general 3D object detection.
Pointcept: a codebase for point cloud perception research. Latest works: PTv3 (CVPR'24 Oral), PPT (CVPR'24), OA-CNNs (CVPR'24), MSC (CVPR'23)
PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
Draco is a library for compressing and decompressing 3D geometric meshes and point clouds. It is intended to improve the storage and transmission of 3D graphics.
A BVH implementation to speed up raycasting and enable spatial queries against three.js meshes.
A command line toolkit to generate maps, point clouds, 3D models and DEMs from drone, balloon or kite images. 📷
Tooling for professional robotic development in C++ and Python with a touch of ROS, autonomous driving and aerospace.
User-friendly, commercial-grade software for processing aerial imagery. 🛩
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Official PyTorch implementation of Superpoint Transformer introduced in [ICCV'23] "Efficient 3D Semantic Segmentation with Superpoint Transformer" and SuperCluster introduced in [3DV'24 Oral] "Scalabl...
[ECCV 2024 Best Paper Candidate] PointLLM: Empowering Large Language Models to Understand Point Clouds
From anything to mesh like human artists. Official impl. of "MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers"
"Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis" (ECCV 2024)
[CVPR 2024] Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysis
[ICRA'24] DeFlow: Decoder of Scene Flow Network in Autonomous Driving
💫 [CVPR 2024] LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis
[ECCV'24] SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving
A curated list of Place Recognition methods, datasets, and various algorithms for LiDAR
3DGS-to-PC: Convert a 3D gaussian splatting scene into a dense point cloud with advanced customisation options and high-accuracy rendered point colours
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
A collection of GTSAM factors and optimizers for point cloud SLAM
Python efficient farthest point sampling (FPS) library. Compatible with numpy.
HeLiMOS: Heterogeneous LiDAR Dataset for Moving Object Segmentation @ IROS'2024
3D Pose Estimation of Two Interacting Hands from a Monocular Event Camera [3DV'24]
Using the KITTI dataset, we employed Open3D to visualize, downsample, segment with RANSAC, cluster via DBSCAN, create 3D bounding boxes, and perform surface reconstruction on point clouds.
Radar4Motion: 4D Imaging Radar based IMU-free Odometry with Radar Cross Section (RCS) weighted Correspondences
PatchAugNet: Patch feature augmentation-based heterogeneous point cloud place recognition in large-scale street scenes
PolyGNN: Polyhedron-based graph neural network for 3D building reconstruction from point clouds [ISPRS 2024]
Official implementation of "Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic Segmentation. Xu et al. ICCV 2023."
A suite of scripts and easy-to-follow tutorial to process point cloud data with Python