AtlasNet

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AtlasNet is a 3D & Animation tool that learns 3D surface generation. It synthesizes meshes from low-resolution point clouds or images using a papier-mâché approach.

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Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is AtlasNet?

AtlasNet is a sophisticated tool designed for learning 3D surface generation, leveraging a unique "papier-mâché" approach. This network is capable of synthesizing a complete mesh, including both point cloud data and connectivity, from various inputs such as low-resolution point clouds or even 2D images. It's an implementation of the research paper "AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation" presented at CVPR 2018. The repository provides source codes, installation instructions, and usage examples for both demo and training purposes. It supports Python 3.6, PyTorch, Pymesh, and Cuda 10.1, making it suitable for researchers and developers in the field of 3D deep learning and computer vision.

Best used for

Ideal for researchers and developers working on 3D deep learning who need to generate complex 3D surfaces from limited input data, reconstruct objects from point clouds, and convert 2D images into 3D models. Especially valuable for academic research and experimental projects in computer vision and geometry processing.

Common actions

generate 3D surfaces

synthesize 3D meshes

process point clouds

convert images to 3D

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Capabilities

Key features

3D surface generation
Mesh synthesis
Point cloud processing
Image-to-3D conversion
PyTorch implementation

Target Audience

researchers in 3d deep learningcomputer vision engineers3d graphics developersacademics studying generative models

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the system requirements for running AtlasNet?

AtlasNet requires Python 3.6, PyTorch, Pymesh, and Cuda 10.1. The repository provides detailed installation instructions, including commands for setting up a Conda environment and installing necessary Python packages and optional components like Chamfer Distance and Metro Distance.

How can I obtain the data required for AtlasNet?

Data download should be automatic, but due to Google Drive traffic caps, manual download might be necessary. Data can be sourced from Google Drive, Kaggle, or NextCloud. You'll need to unzip the archives into specific directories within the AtlasNet folder.

Can AtlasNet generate a mesh directly?

Yes, a recent refactoring in December 2019 added a flag to generate a mesh directly. This enhancement is part of a series of updates aimed at improving the network's efficiency and functionality, including multi-GPU support and object-oriented script rewriting.

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Research & Education › Scientific Computing

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