Trending repositories for topic point-cloud
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)
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
User-friendly, commercial-grade software for processing aerial imagery. 🛩
😎 Awesome LIDAR list. The list includes LIDAR manufacturers, datasets, point cloud-processing algorithms, point cloud frameworks and simulators.
Papers, code and datasets about deep learning for 3D Object Detection.
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
⚡️The spatial perception framework for rapidly building smart robots and spaces
[CVPR 2024, Highlight] Living Scenes: Multi-object Relocalization and Reconstruction in Changing 3D Environments
A curated list of Place Recognition methods, datasets, and various algorithms for LiDAR
This repository is used for automatic calibration between high resolution LiDAR and camera in targetless scenes.
[CVPR 2024, Highlight] Living Scenes: Multi-object Relocalization and Reconstruction in Changing 3D Environments
A curated list of Place Recognition methods, datasets, and various algorithms for LiDAR
Papers, code and datasets about deep learning for 3D Object Detection.
PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
⚡️The spatial perception framework for rapidly building smart robots and spaces
Pointcept: a codebase for point cloud perception research. Latest works: PTv3 (CVPR'24 Oral), PPT (CVPR'24), OA-CNNs (CVPR'24), MSC (CVPR'23)
😎 Awesome LIDAR list. The list includes LIDAR manufacturers, datasets, point cloud-processing algorithms, point cloud frameworks and simulators.
PyTorch implementation of Pointnet2/Pointnet++
This repository is used for automatic calibration between high resolution LiDAR and camera in targetless scenes.
User-friendly, commercial-grade software for processing aerial imagery. 🛩
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
:books: The list of vision-based SLAM / Visual Odometry open source, blogs, and papers
OpenPCDet Toolbox for LiDAR-based 3D Object Detection.
PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
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: Deep Learning on Point Sets for 3D Classification and Segmentation
User-friendly, commercial-grade software for processing aerial imagery. 🛩
😎 Awesome LIDAR list. The list includes LIDAR manufacturers, datasets, point cloud-processing algorithms, point cloud frameworks and simulators.
MapEval: Towards Unified, Robust and Efficient SLAM Map Evaluation Framework.
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. 📷
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.
Tooling for professional robotic development in C++ and Python with a touch of ROS, autonomous driving and aerospace.
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Papers, code and datasets about deep learning for 3D Object Detection.
This repository is used for automatic calibration between high resolution LiDAR and camera in targetless scenes.
MapEval: Towards Unified, Robust and Efficient SLAM Map Evaluation Framework.
A suite of scripts and easy-to-follow tutorial to process point cloud data with Python
[ECCV'24] SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving
[CVPR 2024, Highlight] Living Scenes: Multi-object Relocalization and Reconstruction in Changing 3D Environments
3DGS-to-PC: Convert a 3D gaussian splatting scene into a dense point cloud or basic mesh with advanced customisation options and high-accuracy rendered point colours
[ICRA'24] DeFlow: Decoder of Scene Flow Network in Autonomous Driving
😎 Awesome LIDAR list. The list includes LIDAR manufacturers, datasets, point cloud-processing algorithms, point cloud frameworks and simulators.
A curated list of Place Recognition methods, datasets, and various algorithms for LiDAR
Papers, code and datasets about deep learning for 3D Object Detection.
Robust LiDAR SLAM with a versatile plug-and-play loop closing and pose-graph optimization.
Pointcept: a codebase for point cloud perception research. Latest works: PTv3 (CVPR'24 Oral), PPT (CVPR'24), OA-CNNs (CVPR'24), MSC (CVPR'23)
Accompanying library for the Record3D iOS app (https://record3d.app/). Allows you to receive RGBD stream from iOS devices with TrueDepth camera(s).
3D point cloud datasets in HDF5 format, containing uniformly sampled 2048 points per shape.
[ECCV 2024 Best Paper Candidate] PointLLM: Empowering Large Language Models to Understand Point Clouds
Every authored resources 📦 (premium or open-access) to learn 3D Data Science.
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.
OpenMMLab's next-generation platform for general 3D object detection.
A BVH implementation to speed up raycasting and enable spatial queries against three.js meshes.
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
User-friendly, commercial-grade software for processing aerial imagery. 🛩
A command line toolkit to generate maps, point clouds, 3D models and DEMs from drone, balloon or kite images. 📷
MapEval: Towards Unified, Robust and Efficient SLAM Map Evaluation Framework.
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.
😎 Awesome LIDAR list. The list includes LIDAR manufacturers, datasets, point cloud-processing algorithms, point cloud frameworks and simulators.
Specification for streaming massive heterogeneous 3D geospatial datasets :earth_americas:
3DGS-to-PC: Convert a 3D gaussian splatting scene into a dense point cloud or basic mesh with advanced customisation options and high-accuracy rendered point colours
Every authored resources 📦 (premium or open-access) to learn 3D Data Science.
3DGS-to-PC: Convert a 3D gaussian splatting scene into a dense point cloud or basic mesh with advanced customisation options and high-accuracy rendered point colours
Official dataset and code for "ELMO: Enhanced Real-time LiDAR Motion Capture through Upsampling" and "MOVIN: Real-time Motion Capture using a Single LiDAR"
Official GitHub repo for VecKM. A very efficient and descriptive local geometry encoder / point tokenizer / patch embedder. ICML2024.
MapEval: Towards Unified, Robust and Efficient SLAM Map Evaluation Framework.
[ECCV'24] SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving
Radar4Motion: 4D Imaging Radar based IMU-free Odometry with Radar Cross Section (RCS) weighted Correspondences
A suite of scripts and easy-to-follow tutorial to process point cloud data with Python
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
HeLiMOS: Heterogeneous LiDAR Dataset for Moving Object Segmentation @ IROS'2024
A curated list of Place Recognition methods, datasets, and various algorithms for LiDAR
[Under Review] Efficient and Generic Point Model for Lossless Point Cloud Attribute Compression
A cuda implementation of GPU Poisson Reconstruction.(Data-Parallel Octrees based)
3D DATA PROCESSING AND MODELING ESSENTIALS: A curated collection of tools, literature, courses, and datasets for mastering 3D point cloud technology.
Official implementation of the paper "Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space"
iPhone/iPad -> Unity VFX Graph demo (pre-recorded 3D Video playback)
Source code for the article "GroundGrid: LiDAR Point Cloud Ground Segmentation and Terrain Estimation"
🔥(ECCV 2024 Oral) RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentation
PolyGNN: Polyhedron-based graph neural network for 3D building reconstruction from point clouds [ISPRS 2024]
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
3DGS-to-PC: Convert a 3D gaussian splatting scene into a dense point cloud or basic mesh with advanced customisation options and high-accuracy rendered point colours
[ECCV'24] SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving
[MM 2024] [Need only a 3090] MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors
HeLiMOS: Heterogeneous LiDAR Dataset for Moving Object Segmentation @ IROS'2024
[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
[Under Review] Efficient and Generic Point Model for Lossless Point Cloud Attribute Compression
Every authored resources 📦 (premium or open-access) to learn 3D Data Science.
Official dataset and code for "ELMO: Enhanced Real-time LiDAR Motion Capture through Upsampling" and "MOVIN: Real-time Motion Capture using a Single LiDAR"
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 command line toolkit to generate maps, point clouds, 3D models and DEMs from drone, balloon or kite images. 📷
A BVH implementation to speed up raycasting and enable spatial queries against three.js meshes.
User-friendly, commercial-grade software for processing aerial imagery. 🛩
Tooling for professional robotic development in C++ and Python with a touch of ROS, autonomous driving and aerospace.
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
Source code for the article "GroundGrid: LiDAR Point Cloud Ground Segmentation and Terrain Estimation"
3DGS-to-PC: Convert a 3D gaussian splatting scene into a dense point cloud or basic mesh with advanced customisation options and high-accuracy rendered point colours
[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
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
A collection of GTSAM factors and optimizers for point cloud SLAM
HeLiMOS: Heterogeneous LiDAR Dataset for Moving Object Segmentation @ IROS'2024
Python efficient farthest point sampling (FPS) library. Compatible with numpy.
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.
Radar4Motion: 4D Imaging Radar based IMU-free Odometry with Radar Cross Section (RCS) weighted Correspondences
PolyGNN: Polyhedron-based graph neural network for 3D building reconstruction from point clouds [ISPRS 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.
PatchAugNet: Patch feature augmentation-based heterogeneous point cloud place recognition in large-scale street scenes
[CVPR 2024, Highlight] Living Scenes: Multi-object Relocalization and Reconstruction in Changing 3D Environments
A suite of scripts and easy-to-follow tutorial to process point cloud data with Python