Trending repositories for topic point-cloud
[ECCV 2024 Best Paper Candidate] PointLLM: Empowering Large Language Models to Understand Point Clouds
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
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...
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.
"Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis" (ECCV 2024)
Photogrammetry Guide. Photogrammetry is widely used for Aerial surveying, Agriculture, Architecture, 3D Games, Robotics, Archaeology, Construction, Emergency management, and Medical.
Implementation of the KinectFusion approach in modern C++14 and CUDA
Official repository for paper "Attention-based Point Cloud Edge Sampling" (APES), Highlight@CVPR 2023
Source code for the article "GroundGrid: LiDAR Point Cloud Ground Segmentation and Terrain Estimation"
Morphing and Sampling Network for Dense Point Cloud Completion (AAAI2020)
C++ library and programs for reading and writing ASPRS LAS format with LiDAR data
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
"Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis" (ECCV 2024)
Official repository for paper "Attention-based Point Cloud Edge Sampling" (APES), Highlight@CVPR 2023
[ECCV 2024 Best Paper Candidate] PointLLM: Empowering Large Language Models to Understand Point Clouds
Source code for the article "GroundGrid: LiDAR Point Cloud Ground Segmentation and Terrain Estimation"
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...
Implementation of the KinectFusion approach in modern C++14 and CUDA
[ECCV2022] Masked Autoencoders for Point Cloud Self-supervised Learning
(LMNet) Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data (RAL/IROS 2021)
:thought_balloon: Diffusion Probabilistic Models for 3D Point Cloud Generation (CVPR 2021)
Photogrammetry Guide. Photogrammetry is widely used for Aerial surveying, Agriculture, Architecture, 3D Games, Robotics, Archaeology, Construction, Emergency management, and Medical.
Morphing and Sampling Network for Dense Point Cloud Completion (AAAI2020)
PyTorch implementation of Pointnet2/Pointnet++
An elegant Python interface for visualization on the web platform to interactively generate insights into multidimensional images, point sets, and geometry.
C++ library and programs for reading and writing ASPRS LAS format with LiDAR data
[ECCV 2024 Best Paper Candidate] PointLLM: Empowering Large Language Models to Understand Point Clouds
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
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...
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.
"Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis" (ECCV 2024)
Photogrammetry Guide. Photogrammetry is widely used for Aerial surveying, Agriculture, Architecture, 3D Games, Robotics, Archaeology, Construction, Emergency management, and Medical.
Implementation of the KinectFusion approach in modern C++14 and CUDA
Official repository for paper "Attention-based Point Cloud Edge Sampling" (APES), Highlight@CVPR 2023
Source code for the article "GroundGrid: LiDAR Point Cloud Ground Segmentation and Terrain Estimation"
Morphing and Sampling Network for Dense Point Cloud Completion (AAAI2020)
C++ library and programs for reading and writing ASPRS LAS format with LiDAR data
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
"Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis" (ECCV 2024)
Official repository for paper "Attention-based Point Cloud Edge Sampling" (APES), Highlight@CVPR 2023
[ECCV 2024 Best Paper Candidate] PointLLM: Empowering Large Language Models to Understand Point Clouds
Source code for the article "GroundGrid: LiDAR Point Cloud Ground Segmentation and Terrain Estimation"
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...
Implementation of the KinectFusion approach in modern C++14 and CUDA
[ECCV2022] Masked Autoencoders for Point Cloud Self-supervised Learning
(LMNet) Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data (RAL/IROS 2021)
:thought_balloon: Diffusion Probabilistic Models for 3D Point Cloud Generation (CVPR 2021)
Photogrammetry Guide. Photogrammetry is widely used for Aerial surveying, Agriculture, Architecture, 3D Games, Robotics, Archaeology, Construction, Emergency management, and Medical.
Morphing and Sampling Network for Dense Point Cloud Completion (AAAI2020)
PyTorch implementation of Pointnet2/Pointnet++
An elegant Python interface for visualization on the web platform to interactively generate insights into multidimensional images, point sets, and geometry.
C++ library and programs for reading and writing ASPRS LAS format with LiDAR data
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
OpenMMLab's next-generation platform for general 3D object detection.
[ECCV 2024 Best Paper Candidate] PointLLM: Empowering Large Language Models to Understand Point Clouds
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
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
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. 🛩
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...
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.
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
[NeurIPS 2024 D&B] Point Cloud Matters: Rethinking the Impact of Different Observation Spaces on Robot Learning
🔥(ECCV 2024 Oral) RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentation
A collection of GTSAM factors and optimizers for point cloud SLAM
Radar4Motion: 4D Imaging Radar based IMU-free Odometry with Radar Cross Section (RCS) weighted Correspondences
3DGS-to-PC: Convert a 3D gaussian splatting scene into a dense point cloud with advanced customisation options and high-accuracy rendered point colours
PolyGNN: Polyhedron-based graph neural network for 3D building reconstruction from point clouds [ISPRS 2024]
[ECCV'24] SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving
[ECCV 2024 Best Paper Candidate] PointLLM: Empowering Large Language Models to Understand Point Clouds
HeLiMOS: Heterogeneous LiDAR Dataset for Moving Object Segmentation @ IROS'2024
[MM 2024] [Need only a 3090] MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors
A suite of scripts and easy-to-follow tutorial to process point cloud data with Python
"Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis" (ECCV 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
[TCSVT] CorrI2P: Deep Image-to-Point Cloud Registration via Dense CorrespondenceThe code of CorrI2P
Official repository for paper "Attention-based Point Cloud Edge Sampling" (APES), Highlight@CVPR 2023
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...
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
[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 🏞️🔭
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.
🔥(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
[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...
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
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
Python efficient farthest point sampling (FPS) library. Compatible with numpy.
A collection of GTSAM factors and optimizers for point cloud SLAM
3DGS-to-PC: Convert a 3D gaussian splatting scene into a dense point cloud with advanced customisation options and high-accuracy rendered point colours
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.
HeLiMOS: Heterogeneous LiDAR Dataset for Moving Object Segmentation @ IROS'2024
3D Pose Estimation of Two Interacting Hands from a Monocular Event Camera [3DV'24]
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
A suite of scripts and easy-to-follow tutorial to process point cloud data with Python
Official implementation of "Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic Segmentation. Xu et al. ICCV 2023."
