8 रिपॉजिटरी
Software libraries for manipulating, visualizing, and analyzing 3D point cloud data.
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Open3D is a 3D data processing library, visualization engine, and machine learning library. It provides a framework for manipulating point clouds and meshes through specialized algorithms designed for 3D data science workflows. The project includes a toolkit for 3D scene reconstruction to generate spatial models and align surfaces from raw data. It also functions as a GPU accelerated framework that offloads intensive spatial computations to the graphics processor to increase processing speed. The library covers a broad range of capabilities including physically based light simulations for vi
Library for 3D data processing, visualization, and algorithm development.
The Point Cloud Library is a collection of C++ algorithms designed for filtering, registering, and analyzing large-scale 3D spatial datasets. It provides a framework for 3D point cloud processing, incorporating tools for spatial data filtering and geometric feature estimation. The library includes specialized systems for aligning multiple spatial datasets into a single unified coordinate system and a rendering engine for the visual inspection and analysis of processed point cloud data. It also features tools for calculating spatial descriptors to identify structural patterns and shapes within
Highly parallel library for industrial and research point cloud processing.
PyTorch3D is a 3D geometric deep learning library and mesh processing toolkit designed for learning from point clouds and complex 3D surface geometries. It provides a collection of reusable components and data structures for deep learning with 3D data, including a framework for training and evaluating neural radiance fields to enable photorealistic view synthesis. The project features a differentiable 3D renderer that converts meshes and point clouds into 2D images while allowing gradients to flow back into the geometry and textures. This enables 3D shape optimization, where mesh geometry, te
Deep learning library for 3D data processing and research.
Kaolin एक PyTorch 3D डीप लर्निंग लाइब्रेरी है जो 3D ज्यामिति प्रसंस्करण, भौतिकी सिमुलेशन, डेटा विज़ुअलाइज़ेशन, और कंप्यूटर विज़न के लिए ग्रेडिएंट-आधारित रेंडरिंग के लिए टूल का एक व्यापक सूट प्रदान करती है। इस लाइब्रेरी में एक डिफरेंशिएबल 3D रेंडरर और मेश और पॉइंट क्लाउड जैसे 3D अभ्यावेदन को बदलने और बदलने के लिए एक ज्यामिति प्रसंस्करण टूलकिट शामिल है। इसमें त्रि-आयामी वस्तुओं और दृश्यों के बीच भौतिक इंटरैक्शन और टकराव की गणना करने के लिए एक 3D भौतिकी सिमुलेशन इंजन भी है। यह टूलकिट 3D डेटा विज़ुअलाइज़ेशन के लिए यूटिलिटीज प्रदान करती है, जिसमें इंटरैक्टिव दृश्य और टर्नटेबल एनिमेशन का निर्माण शामिल है। अतिरिक्त क्षमताएं 3D डेटासेट प्रबंधन, डेटा प्रीप्रोसेसिंग, और 3D अभ्यावेदन रेंडरिंग को कवर करती हैं।
NVIDIA library for accelerating 3D deep learning research.
PyVista is a scientific 3D plotting framework and visualization library that provides a Python interface for rendering and analyzing spatial datasets using a VTK backend. It functions as a volumetric rendering engine and a 3D mesh analysis tool for computing geometric properties and performing boolean operations on surface and volumetric meshes. The project is distinguished by its ability to operate as a headless 3D renderer, generating high-quality renders and animations on remote servers without a physical display. It also features a lazy-accessor extension mechanism that allows the registr
Interface for 3D plotting and mesh analysis using VTK.
Making point clouds fun again
Python library for scientific point cloud data manipulation.
efficient tools for LiDAR processing
Command-line tools and C++ library for point cloud processing.
pointcloudset
Efficient analysis of time-series point cloud datasets.