This is the Linux app named PyTorch3D whose latest release can be downloaded as Version0.7.8sourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named PyTorch3D with OnWorks for free.
Siga estas instrucciones para ejecutar esta aplicación:
- 1. Descargue esta aplicación en su PC.
- 2. Ingrese en nuestro administrador de archivos https://www.onworks.net/myfiles.php?username=XXXXX con el nombre de usuario que desee.
- 3. Cargue esta aplicación en dicho administrador de archivos.
- 4. Inicie el emulador en línea OnWorks Linux o Windows en línea o el emulador en línea MACOS desde este sitio web.
- 5. Desde el SO OnWorks Linux que acaba de iniciar, vaya a nuestro administrador de archivos https://www.onworks.net/myfiles.php?username=XXXXX con el nombre de usuario que desee.
- 6. Descarga la aplicación, instálala y ejecútala.
SCREENSHOTS
Ad
PyTorch3D
DESCRIPCIÓN
PyTorch3D is a comprehensive library for 3D deep learning that brings differentiable rendering, geometric operations, and 3D data structures into the PyTorch ecosystem. It’s designed to make it easy to build and train neural networks that work directly with 3D data such as meshes, point clouds, and implicit surfaces. The library provides fast GPU-accelerated implementations of rendering pipelines, transformations, rasterization, and lighting—making it possible to compute gradients through full 3D rendering processes. Researchers use it for tasks like shape generation, reconstruction, view synthesis, and visual reasoning. PyTorch3D also includes utilities for loading, transforming, and sampling 3D assets, so models can be trained end-to-end from 2D supervision or partial data. Its modular design allows easy extension—components like differentiable rasterizers, mesh blending, or signed distance field (SDF) modules can be swapped or combined to test new architectures quickly.
Caracteristicas
- Differentiable rendering for meshes, point clouds, and implicit surfaces
- GPU-accelerated rasterization and lighting operations
- Modular design for composable 3D pipelines and easy experimentation
- Utilities for loading, transforming, and augmenting 3D datasets
- Integration with PyTorch autograd for gradient-based optimization in 3D
- Ready-to-use functions for shape reconstruction, view synthesis, and generation
Lenguaje de programación
Python
Categorías
This is an application that can also be fetched from https://sourceforge.net/projects/pytorch3d.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
