A point cloud is a set of data points in space. The points represent a 3D shape or object. Each point has its set of X, Y and Z coordinates.
PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
Photogrammetry Guide. Photogrammetry is widely used for Aerial surveying, Agriculture, Architecture, 3D Games, Robotics, Archaeology, Construction, Emergency management, and Medical.
PointCNN: Convolution On X-Transformed Points (NeurIPS 2018)
Reconstruct Watertight Meshes from Point Clouds [SIGGRAPH 2020]
[NeurIPS'22] PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
Latent Point Diffusion Models for 3D Shape Generation
Image Signal Processing (ISP) Guide. Learn all about the process of converting an image/video into digital form by performing tasks like noise reduction, filtering, auto exposure, autofocus, HDR correction, and image sharpening with a Specialized type of media processor.
A Python command-line utility for converting and processing 3D Gaussian Splatting models. Supports N-to-N conversion between 3DGS (.ply), KSplat, SOG, SPZ, Splat, CloudCompare, Parquet, and Compressed PLY. Includes GPU-accelerated filtering (SOR, Density).
[ICLR 2022 poster] Official PyTorch implementation of "Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework"
Full-python LiDAR SLAM using ICP and Scan Context
[CVPR 2020 Oral] A differentiable framework for 3D registration
Laspy is a pythonic interface for reading/modifying/creating .LAS LIDAR files matching specification 1.0-1.4.
OpenPoints: a library for easily reproducing point-based methods for point cloud understanding. The engine for [ASSANet](https://arxiv.org/abs/2110.10538), [PointNeXt](https://arxiv.org/abs/2206.04670)
[ICRA 2022] CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation
[ECCV 2026] Track4World: Feedforward World-centric Dense 3D Tracking of All Pixels
This repository is an open-source PointPainting package which is easy to understand, deploy and run!
[CVPR 2021] SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration
🔥 Synthetic and real-world 2d/3d dataset for semantic and instance segmentation (BMVC 2022 Oral)
[PyTorch] Official implementation of CVPR2021 paper "PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency". https://arxiv.org/abs/2103.05465
[TensorFlow] Official implementation of CVPR'20 oral paper - D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features https://arxiv.org/abs/2003.03164