NVIDIAGameWorks/kaolin
Kaolin
Kaolin ist eine PyTorch-3D-Deep-Learning-Bibliothek, die eine umfassende Suite an Tools für 3D-Geometrieverarbeitung, Physiksimulation, Datenvisualisierung und gradientenbasiertes Rendering für Computer Vision bereitstellt.
Die Bibliothek enthält einen differenzierbaren 3D-Renderer und ein Toolkit zur Geometrieverarbeitung für die Konvertierung und Transformation von 3D-Repräsentationen wie Meshes und Punktwolken. Sie verfügt zudem über eine 3D-Physiksimulations-Engine zur Berechnung physikalischer Interaktionen und Kollisionen zwischen dreidimensionalen Objekten und Szenen.
Das Toolkit bietet Utilities für die 3D-Datenvisualisierung, einschließlich der Erstellung interaktiver Ansichten und Turntable-Animationen. Zusätzliche Funktionen decken das 3D-Datenmanagement, die Datenvorverarbeitung und das Rendering von 3D-Repräsentationen ab.
Features
- Differentiable Rasterizers - Ships a differentiable rasterizer that allows gradients to flow from 2D images back into 3D spatial parameters.
- PyTorch-Based Frameworks - Acts as a PyTorch-based framework for accelerating research in 3D computer vision and deep learning.
- PyTorch Tensor Operations - Performs all 3D geometric calculations using PyTorch tensor operations for GPU acceleration and automatic differentiation.
- 3D Point Cloud Learning - Enables deep learning research on 3D geometric data including meshes and point clouds using PyTorch.
- 3D Physics Engines - Includes a 3D physics engine to calculate physical interactions and collisions between objects and scenes.
- 3D Math and Geometry Toolkits - Provides a comprehensive toolkit for converting, managing, and transforming 3D representations such as meshes and point clouds.
- Differentiable Rendering - Provides a differentiable rendering pipeline allowing 3D geometry optimization via gradients from 2D images.
- CUDA Compute Kernels - Provides custom CUDA compute kernels in C++ to parallelize high-speed 3D physics and spatial transformations.
- 3D Representation Conversions - Provides utilities for transforming geometric data between formats like meshes, point clouds, and volumetric structures.
- Coordinate-Based Spatial Mappings - Implements coordinate-based spatial mapping to represent 3D geometry as functions or grids in Cartesian space.
- Voxel-Based Grids - Organizes spatial data into voxel-based grids to optimize collision detection and volumetric operations.
- Mesh-To-Pointcloud Sampling - Provides weighted area sampling to convert surface meshes into discrete point sets for deep learning input.
- 3D Spatial Preprocessing - Implements 3D spatial preprocessing pipelines to transform data formats for improved deep learning training speed.
- Physical Interaction Simulations - Simulates physical interactions and collisions between meshes and point clouds within 3D scenes.
- 3D Model Visualizers - Offers tools for rendering and inspecting 3D models with interactive views and turntable animations.
- 3D Scene Renderers - Produces 2D images from spatial data using lighting and camera models to support gradient-based optimization.
- Data and Graph Processing - Library for 3D deep learning research.
- Punktwolkenverarbeitung - PyTorch library for accelerating 3D deep learning research.
- Developer Tools - 3D deep learning research library.
- Processing Libraries - NVIDIA library for accelerating 3D deep learning research.
- D Segmentation, Classification and Regression - Listed in the “D Segmentation, Classification and Regression” section of the The Incredible Pytorch awesome list.
