Point Clouds
Showing 32 of 32 projects
A PyTorch library for building and training Graph Neural Networks (GNNs) on structured and irregular data.
A comprehensive library for building and training Graph Neural Networks (GNNs) with PyTorch.
An open-source SDK for logging, storing, querying, and visualizing multimodal, time-series data like images, point clouds, and tensors.
A curated repository of resources, datasets, and research papers for 3D machine learning, covering computer vision, graphics, and deep learning.
A library for compressing and decompressing 3D geometric meshes and point clouds to improve storage and transmission.
A curated list of papers, datasets, and code for 3D point cloud analysis research, covering classification, segmentation, detection, and more.
An open specification for streaming massive heterogeneous 3D geospatial datasets across desktop, web, and mobile applications.
A modular C++ library implementing the Iterative Closest Point (ICP) algorithm for aligning 2D and 3D point clouds in robotics and computer vision.
An end-to-end 3D object detection network that uses deep point set networks and Hough voting to directly detect objects in point clouds.
A deep learning pipeline for 3D object detection from RGB-D data by combining 2D detectors with PointNet-based 3D processing.
An efficient neural network for semantic segmentation of large-scale 3D point clouds using random sampling.
A Python library for 3D point cloud processing that leverages the scientific Python stack for complex operations with minimal code.
A Blender addon for importing photogrammetry and NeRF data from various reconstruction libraries and point cloud formats.
A PyTorch implementation for super fast and accurate 3D object detection using LiDAR point clouds, featuring an anchor-free approach.
A framework for semantic and instance segmentation of LiDAR point clouds using range images, designed for autonomous driving applications.
A PyTorch framework for efficient 3D semantic and panoptic segmentation using superpoint-based transformer architectures.
A desktop tool for labeling individual points and polygons in LiDAR point cloud datasets, specifically designed for KITTI format.
A deprecated JavaScript library for generating concave hulls from sets of points.
Utility scripts for loading, visualizing, and inspecting the KITTI-360 autonomous driving dataset.
A lightweight neural network for near-real-time semantic segmentation of LiDAR point clouds using polar coordinate quantization.
A header-only C++ library for reading and writing PLY files, with automatic type promotion and mesh-specific helpers.
A curated list of deep learning resources for 3D shape processing, covering classification, reconstruction, and generation.
A real-time fiducial tag system for LiDAR point clouds, robust to lighting and compatible with visual markers like AprilTags.
An open-source platform for storing, visualizing, and sharing geospatial data like orthophotos, point clouds, and 3D models.
A PyTorch framework for deep learning on point clouds, providing a modular and reproducible foundation for 3D vision tasks.
A large-scale driving behavior dataset with LiDAR point clouds, dashboard videos, and sensor data for autonomous driving research.
C++ libraries for multi primitive-to-primitive ICP algorithms and flexible point cloud processing pipelines.
A deep learning approach that unifies global place recognition and local 6DoF pose refinement for robust relocalization in large-scale 3D point clouds.
A 3D object detection method that exploits visibility information from LiDAR point clouds to improve accuracy.
A curated collection of resources, datasets, and methods for 3D LiDAR-based Moving Object Segmentation (MOS) research.
A Python package for 3D single-cell shape analysis using geometric deep learning on point clouds and voxels.
Python implementation of scan unfolding for KITTI LiDAR data to create dense cylindrical projections without systematic discretization artifacts.
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