GET3D (Nvidia)

GET3D, developed by Nvidia, is an AI tool that generates explicit textured 3D meshes with rich geometric details, complex topology, and high-fidelity textures. Its unique feature is its ability to generate diverse shapes with arbitrary topology, high-quality geometry, and texture. The tool is designed to meet the demands of various industries that require scalable, diverse, and high-quality 3D content creation tools.

GET3D's foundation is based on years of research in differentiable rendering, deep learning neural networks, and 3D reconstruction. The model is trained with adversarial losses defined on 2D images, making it an exceptional tool in 3D generative modeling. With GET3D, users can create high-quality 3D textured meshes that can be immediately consumed by 3D rendering engines, making them usable in downstream applications.

TLDR

GET3D is an AI tool developed by Nvidia that generates 3D explicit textured meshes with complex topology, rich geometric details, and high-fidelity textures. It offers diverse shape generation, direct usability in downstream applications, and exceptional training on 2D images. GET3D could benefit industries such as gaming, virtual reality, architecture, film, and scientific research.

The tool offers a range of features, including real-time object detection, image segmentation, 3D object detection, automated data labeling, and real-time pose estimation. GET3D is easy to use, efficient, and compatible with various operating systems and programming languages.

Company Overview

GET3D, an AI tool developed by Nvidia, is a generative model that directly generates explicit textured 3D meshes with complex topology, rich geometric details, and high-fidelity textures. The tool generates a 3D Signed Distance Function (SDF) and a texture field via two latent codes. It utilizes DMTet to extract a 3D surface mesh from the SDF and queries the texture field at surface points to get colors.

The model is trained with adversarial losses defined on 2D images. In particular, it uses a rasterization-based differentiable renderer to obtain RGB images and silhouettes. GET3D is able to generate diverse shapes with arbitrary topology, high-quality geometry, and texture.

This feature could be valuable to industries that require content creation tools that can scale in terms of the quantity, quality, and diversity of 3D content.

The AI tool aims to synthesize textured meshes that can be directly consumed by 3D rendering engines, making them immediately usable in downstream applications. Prior works on 3D generative modeling have been limited in terms of the mesh topology they can produce, typically do not support textures or lack geometric details. However, GET3D bridges the recent success in differentiable surface modeling, differentiable rendering, and 2D Generative Adversarial Networks, enabling the tool to train from 2D image collections.

GET3D is exemplified by significant improvements over previous methods. The tool is capable of generating high-quality 3D textured meshes, including cars, chairs, animals, motorbikes, human characters, and buildings. The model harmonizes well with other AI tools, such as DIBR++, to create materials and produce meaningful view-dependent lighting effects in an unsupervised manner.

Text-guided shape generation is also achievable with the tool, with users providing text prompts that the model can finetune using directional CLIP loss on rendered 2D images and texts.

The foundation for GET3D builds upon years of research in differentiable rendering, deep learning neural networks, and 3D reconstruction. The tool is founded upon work such as the Learning Deformable Tetrahedral Meshes for 3D Reconstruction and Deep Marching Tetrahedra initiatives from NeurIPS 2020 and 2021, respectively. GET3D is also supported by Extracting Triangular 3D Models, Materials, and Lighting From Images, EG3D: Efficient Geometry-aware 3D Generative Adversarial Networks, and DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer initiatives.

Built to satisfy the needs of industries that require robust and diverse 3D models, GET3D offers an outstanding tool for 3D content creators. For licensing and other business inquiries, visit NVIDIA's website to access the submission form.

Features

Real-Time Object Detection

Fast and Accurate Detection

GET3D offers a real-time object detection feature that is highly accurate and fast. The tool can detect and classify multiple objects in an image with high precision and speed, thanks to its deep learning algorithms. This feature provides a highly accurate way of detecting objects in real-time, ideal for applications such as surveillance, autonomous driving, and robotics.

Cross-Platform Support

The tool is cross-platform and can be used with Windows, macOS, and Linux operating systems. It's also compatible with various programming languages such as C++, Python, and MATLAB, making it easy to integrate into any application or system.

Easy to Train

The object detection feature is easy to train using a wide range of datasets, including COCO, KITTI, and ImageNet, among others. Users can create custom datasets and fine-tune their models using transfer learning, enabling them to improve the detection accuracy even further.

Image Segmentation

Highly Accurate Results

GET3D offers a powerful image segmentation feature, which can accurately segment images into various categories. This feature uses state-of-the-art deep learning algorithms that can deliver highly accurate results even in challenging environments.

Multi-Class Segmentation

The tool supports multi-class segmentation, allowing users to segment images into multiple categories. This feature is ideal for tasks such as medical image analysis or autonomous driving, where images need to be classified into multiple classes for better decision-making processes.

Real-Time Segmentation

GET3D's image segmentation feature delivers real-time segmentation, enabling users to process images in real-time. This feature can segment images in real-time videos, making it suitable for a wide range of applications such as video surveillance and object tracking.

3D Object Detection

Highly Accurate and Precise

The tool offers a 3D object detection feature that is highly accurate and precise. The feature uses cutting-edge algorithms to detect and locate 3D objects in real-time, making it ideal for tasks such as robotics and autonomous vehicles.

It can detect and segment objects, such as cars, pedestrians, and traffic signs, with high accuracy and precision.

Efficient Training

The 3D object detection feature is easy to train, and users can fine-tune the model to improve detection accuracy. It requires minimal training data, thanks to its efficient algorithms, making it an ideal tool for small businesses and startups looking for an efficient way to perform 3D object detection.

Multi-View Detection

GET3D supports multi-view detection, allowing users to detect and locate objects from different viewpoints. This feature can locate objects from multiple viewpoints, making it suitable for tasks such as autonomous driving, where objects need to be located from different viewpoints for safe driving.

Automated Data Labeling

Efficient Data Annotation

GET3D offers an automated data labeling feature, which allows users to annotate large datasets with minimal efforts. This feature uses advanced machine learning algorithms to detect and annotate images, enabling users to save time and resources.

Custom Annotations

The automated data labeling feature enables users to create custom annotations for different labeling tasks. It supports a wide range of annotation types, including bounding boxes, polygons, and points, among others.

Integration with Other Tools

GET3D's automated data labeling feature is compatible with various data labeling tools, including Labelbox, Dataturks, and Amazon Mechanical Turk, making it easy to integrate into any data labeling workflow.

Real-Time Pose Estimation

Highly Accurate Pose Estimation

The tool offers a real-time pose estimation feature, enabling users to detect and estimate human poses in real-time videos. This feature uses deep learning algorithms to deliver highly accurate results and is ideal for applications such as security surveillance, sports analysis, and fitness tracking.

Multi-Person Pose Estimation

GET3D supports multi-person pose estimation, enabling users to detect multiple poses in real-time videos. This feature is ideal for applications such as group fitness tracking or security surveillance, where multiple people need to be tracked simultaneously.

Low Latency

The real-time pose estimation feature has minimal latency, making it possible to estimate poses in real-time. This feature is ideal for tasks that require real-time decision-making, such as autonomous driving and robotics.

FAQ

What is GET3D?

GET3D is an AI tool developed by Nvidia and is a generative model that generates 3D Signed Distance Function (SDF) and texture fields via two latent codes to directly generate 3D explicit textured meshes with complex topology, geometric details, and textures.

What distinguishes GET3D from other generative AI models?

GET3D is a direct generator of textured 3D meshes that can be immediately consumed by 3D rendering engines. Other generative AI models have limitations in terms of mesh topology, lack of geometric details, or limited support for textures. GET3D is designed to create diverse shapes with arbitrary topology, high-quality geometry, and texture.

What industries can GET3D be beneficial for?

GET3D can be valuable to industries that require content creation tools that can scale in quantity, quality, and diversity of 3D content. Industries such as gaming, virtual reality, architecture, film, and scientific research that require robust and diverse 3D models could benefit from this tool.

What kind of 3D models can GET3D generate?

GET3D is capable of generating high-quality 3D textured meshes, including cars, chairs, animals, motorbikes, human characters, and buildings. Text-guided shape generation is also achievable with the tool.

What is the foundation of GET3D based on?

GET3D's foundation builds upon years of research in differentiable rendering, deep learning neural networks, and 3D reconstruction. It is founded upon works such as Learning Deformable Tetrahedral Meshes for 3D Reconstruction, Deep Marching Tetrahedra, Extracting Triangular 3D Models Materials, and Lighting From Images, EG3D: Efficient Geometry-aware 3D Generative Adversarial Networks, and DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer.