GET3D

GET3D

NVIDIA
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About

We generate a 3D SDF and a texture field via two latent codes. We utilize DMTet to extract a 3D surface mesh from the SDF and query the texture field at surface points to get colors. We train with adversarial losses defined on 2D images. In particular, we use a rasterization-based differentiable renderer to obtain RGB images and silhouettes. We utilize two 2D discriminators, each on RGB image, and silhouette, respectively, to classify whether the inputs are real or fake. The whole model is end-to-end trainable. As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of 3D content is becoming evident. In our work, we aim to train performant 3D generative models that synthesize textured meshes which can be directly consumed by 3D rendering engines, thus immediately usable in downstream applications.

About

SpAItial is an AI platform focused on building and deploying Spatial Foundation Models (SFMs), a new class of generative AI systems designed to create and understand 3D environments with physical realism and spatial awareness. Unlike traditional models that generate pixels or text independently, SpAItial’s technology operates directly on 3D structures, capturing geometry, materials, lighting, and physics from the outset to produce coherent, interactive worlds. Its flagship model, Echo-2, can transform a single image into a fully explorable, photorealistic 3D scene using techniques like Gaussian splatting, enabling users to navigate and render environments in real time. It is built around a physically grounded understanding of space-time, allowing AI to reason about how objects exist, interact, and evolve within an environment rather than producing disconnected outputs. This approach reduces inconsistencies common in traditional generative AI and enables more accurate simulation.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Anyone seeking a generative model of high quality 3D textured shapes learned from images

Audience

Developers and creators building immersive 3D, AR/VR, or robotics applications who need AI that can generate and reason about realistic spatial environments from minimal input

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

NVIDIA
United States
nv-tlabs.github.io/GET3D/

Company Information

spAItial
United States
app.spaitial.ai/

Alternatives

Seed3D

Seed3D

ByteDance

Alternatives

SuperSplat

SuperSplat

PlayCanvas
Spatial Studio

Spatial Studio

Real Horizons
Splat Labs

Splat Labs

ROCK Robotic
Seed3D

Seed3D

ByteDance
Genie 3

Genie 3

Google DeepMind

Categories

Categories

Integrations

No info available.

Integrations

No info available.
Claim GET3D and update features and information
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