vgg-ttt

nvidia/vgg-ttt

vgg-ttt is a image-to-3d model from nvidia. Use it for the image-to-3d task on the model card, and read the license before you ship it in a product. It is set up for vggttt. The card lists the license as other.

<div align="center" <h1VGG-T³: Offline Feed-Forward 3D Reconstruction at Scale</h1

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At a glance

Task: Image-to-3D
Library: vggttt
License: other
Access: Public
Created: Dec 10, 2025
Updated: May 30, 2026
SHA: 2bd0869b

Base models

facebook/VGGT-1B

Model card

From the Hugging Face model README

<div align="center"> <h1>VGG-T³: Offline Feed-Forward 3D Reconstruction at Scale</h1>

NVIDIA University of Toronto Vector Institute

Sven Elflein, Ruilong Li, Sérgio Agostinho, Zan Gojcic, Laura Leal-Taixé, Qunjie Zhou, Aljosa Osep

</div>

Model Overview

Description

VGG-T³ reconstructs 3D geometry and camera parameters from image collections and videos. The model scales linearly with the number of input images, which significantly accelerates reconstruction when dealing with a large number of images or long videos.

This model is for research and development only.

License/Terms of Use

This model is released under the NVIDIA OneWay Noncommercial License. It may only be used for non-commercial research or educational purposes.

Deployment Geography

Global

Use Case

Primary users:

Computer Vision Researchers: For benchmarking 3D reconstruction and developing neural rendering pipelines.
Augmented Reality/Virtual Reality & Robotics Engineers: For implementing real-time simultaneous localization and mapping (SLAM), scene understanding, and navigation systems.
3D Content Creators: For rapid conversion of video/images into 3D assets.

Primary Use Cases

3D Reconstruction: Fast, feed-forward estimation of sparse 3D geometry and camera poses from unposed images or video without iterative optimization.
Structure-from-motion (SfM) Replacement: Accelerating initialization for 3D Gaussian Splatting and Neural Radiance Field (NeRF) training by replacing slow SfM (e.g., COLMAP) steps.
Robotic Perception: Instant inference of "pointmaps" (pixel-to-3D coordinates) for manipulation and spatial awareness.

Release Date

GitHub 05/25/2026 via https://github.com/nv-dvl/vgg-ttt Hugging Face 05/25/2026 via https://huggingface.co/nvidia/vgg-ttt

References

Pre-print
Base model: VGGT: Visual Geometry Grounded Transformer

Quick Start

Install the code (preferably in a conda environment):

git clone https://github.com/nv-dvl/vgg-ttt.git && cd vgg-ttt
pip install torch==2.7.1 torchvision==0.22.1 --index-url https://download.pytorch.org/whl/cu126
pip install -r requirements.txt

VGG-T³ is compatible with the VGGT API and can be used in a similar way:

from vggttt.nets.vggt.models.vggt import VGGT
from vggttt.nets.vggt.img import load_and_preprocess_images

vggttt = VGGT.from_pretrained("nvidia/vgg-ttt").eval().cuda()

image_names = ["path/to/imageA.png", "path/to/imageB.png", "path/to/imageC.png"]
images = load_and_preprocess_images(image_names).to("cuda")

preds = vggttt.infer(images)
# Dict containing the predicted outputs with the following keys:
#  - 'pose':        [#images, 4, 4]  Camera-to-world transformation
#  - 'intrinsics':  [#images, 3, 3]  Pinhole camera matrix
#  - 'pts3d':       [#images, height, width, 3]  Per-pixel points in world coordinates
#  - 'conf':        [#images, height, width]  Per-pixel confidence in range ]1, inf[
#  - 'depth':       [#images, height, width, 1]  Per-pixel depth

Model Architecture

Architecture Type: Transformer Network Architecture: Vision-Transformer (ViT) This model was developed based on: VGGT Number of model parameters: 1.19 × 10⁹

Input(s)

Input Type(s): Image, video

Input Format(s):

Image: Red, Green, Blue (RGB)
Video: .mov, .mp4

Input Parameters:

Video: Three-Dimensional (3D)
Collection of images: Three-Dimensional (3D)

Other Properties Related to Input:

Video is converted to images at frame rate of the video.
Maximum resolution per image: 518x518

Output(s)

Output Type(s):

Camera parameters (extrinsics, intrinsics)
Point cloud (3D point per pixel)

Output Format(s):

Point cloud: X, Y, Z
Camera intrinsics: Focal length
Camera extrinsics: Rotation matrix, translation vector

Output Parameters:

Point cloud: Three-Dimensional (3D)
Camera intrinsics: Two-Dimensional (2D)
Camera extrinsics: Three-Dimensional (3D)

Other Properties Related to Output:

One 3D point is predicted per input pixel in each image.
One set of camera parameters (intrinsics + extrinsics) is predicted per image.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration

Runtime Engine(s): N/A

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Blackwell
NVIDIA Hopper
NVIDIA Volta

Preferred/Supported Operating System(s): Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s)

v1.0: Initial release

Training, Testing, and Evaluation Datasets

Training Datasets

DynamicReplica (DynamicStereo)

Link: https://dynamic-stereo.github.io/

Data Modality: Image, Depth, Camera extrinsics and intrinsics.

Image Training Data Size [Less than a Million Images]

Data Collection Method by dataset [Synthetic]

Labeling Method by dataset [Synthetic]

Properties: 145,200 stereo frames (from 524 videos). The dataset contains synthetic rendered videos of scanned environments with dynamic objects (people/animals). It includes stereo images, disparity maps, and camera parameters.

Hypersim

Link: https://github.com/apple/ml-hypersim

Data Modality: Image, Depth, Camera extrinsics and intrinsics.

Image Training Data Size [Less than a Million Images]

Data Collection Method by dataset [Synthetic]

Labeling Method by dataset [Synthetic]

Properties: ~77,400 images. Photorealistic synthetic dataset for indoor scene understanding. Contains RGB, depth, semantic segmentation, and camera parameters.

OmniData

Link: https://github.com/EPFL-VILAB/omnidata/tree/main/omnidata_tools/dataset#readme

Data Modality: Image, Depth, Camera extrinsics and intrinsics.

Image Training Data Size [1 Million to 1 Billion Images]

Data Collection Method by dataset [Hybrid: Automatic/Sensors, Synthetic]

Labeling Method by dataset [Synthetic]

Properties: ~14 million rendered images from various meshes that are partially obtained from real world scans.

CubifyAnything

Link: https://github.com/apple/ml-cubifyanything

Data Modality: Image, Depth, Camera extrinsics and intrinsics.

Image Training Data Size [1 Million to 1 Billion Images]

Data Collection Method by dataset [Automatic/Sensors]

Labeling Method by dataset [Hybrid: Human, Automatic/Sensors]

Properties: The dataset aligns handheld RGB-D captures with laser scans, creating a large-scale labeled dataset for 3D object detection.

ScanNet++

Link: https://scannetpp.mlsg.cit.tum.de/scannetpp/

Data Modality: Image, Depth, Camera extrinsics and intrinsics.

Image Training Data Size [1 Million to 1 Billion Images]

Data Collection Method by dataset [Human]

Labeling Method by dataset [Hybrid: Human, Automatic/Sensors]

Properties: Millions of frames (video) / ~460 scenes. High-resolution real-world 3D indoor semantic scene understanding dataset using laser scanning and DSLR/iPhone capture.

Mapillary Metropolis

Link: https://www.mapillary.com/dataset/metropolis

Data Modality: Image, Depth, Camera extrinsics and intrinsics.

Image Training Data Size [Less than a Million Images]

Data Collection Method by dataset [Automatic/Sensors]

Labeling Method by dataset [Hybrid: Human, Automatic/Sensors]

Properties: ~28,000 images. City-scale dataset containing street-level panoramic images, LiDAR point clouds, and aerial imagery.

MatrixCity

Link: https://city-super.github.io/matrixcity/

Data Modality: Image, Depth, Camera extrinsics and intrinsics.

Image Training Data Size [Less than a Million Images]

Data Collection Method by dataset [Synthetic]

Labeling Method by dataset [Synthetic]

Properties: ~67,000 images (Small City block). Large-scale synthetic city dataset for neural rendering, generated using Unreal Engine 5.

MegaDepth

Link: https://www.cs.cornell.edu/projects/megadepth/

Data Modality: Image, Depth, Camera extrinsics and intrinsics.

Image Training Data Size [Less than a Million Images]

Data Collection Method by dataset [Automated]

Labeling Method by dataset [Automatic/Sensors]

Properties: ~150,000 reconstructed images. Derived from Internet photo collections (Flickr) using Structure-from-Motion (SfM) and Multi-View Stereo (MVS) to generate depth maps.

Mid-Air

Link: https://midair.ulg.ac.be/

Data Modality: Image, Depth, Camera extrinsics and intrinsics.

Image Training Data Size [Less than a Million Images]

Data Collection Method by dataset [Synthetic]

Labeling Method by dataset [Synthetic]

Properties: ~420,000 video frames. Synthetic dataset for low-altitude drone flight, featuring multi-modal data (RGB, depth, normals, stereo) across different climate conditions.

Mapillary Planet-scale Depth dataset

Link: https://www.mapillary.com/dataset/depth

Data Modality: Image, Depth, Camera extrinsics and intrinsics.

Image Training Data Size [Less than a Million Images]

Data Collection Method by dataset [Hybrid: Human, Automatic/Sensors]

Labeling Method by dataset [Automatic/Sensors]

Properties: ~750,000 images. Dense depth dataset derived from street-level imagery using SfM, covering a wide variety of locations and camera models.

ParallelDomain4D

Link: https://github.com/basilevh/gcd?tab=readme-ov-file#paralleldomain-4d

Data Modality: Image, Depth, Camera extrinsics and intrinsics.

Image Training Data Size [1 Million to 1 Billion Images]

Data Collection Method by dataset [Synthetic]

Labeling Method by dataset [Synthetic]

Properties: Synthetic video dataset rendered using the Parallel Domain engine. Designed for dynamic novel view synthesis and 4D reconstruction.

Virtual KITTI 2

Link: https://europe.naverlabs.com/proxy-virtual-worlds-vkitti-2/

Data Modality: Image, Depth, Camera extrinsics and intrinsics.

Image Training Data Size [Less than a Million Images]

Data Collection Method by dataset [Synthetic]

Labeling Method by dataset [Synthetic]

Properties: Photorealistic synthetic video dataset mimicking the real KITTI dataset, used for object detection, tracking, and depth estimation.

Waymo Open Dataset

Link: https://waymo.com/open/

Data Modality: Image, Depth, Camera extrinsics and intrinsics.

Image Training Data Size [1 Million to 1 Billion Images]

Data Collection Method by dataset [Hybrid: Human, Automatic/Sensors]

Labeling Method by dataset [Hybrid: Human for semantic annotations, Automatic/Sensors for depth]

Properties: Large-scale autonomous driving dataset collected with high-resolution cameras and LiDAR.

Common Objects in 3D (CO3Dv2)

Link: https://ai.meta.com/datasets/co3d-dataset/

Data Modality: Image, Depth, Camera extrinsics and intrinsics.

Image Training Data Size [1 Million to 1 Billion Images]

Data Collection Method by dataset [Human]

Labeling Method by dataset [Automatic/Sensors]

Properties: ~6 million images (v2 version of the dataset). Large-scale dataset of object-centric videos captured with smartphones, annotated with camera poses and point clouds via structure-from-motion.

Kubric

Link: https://github.com/google-research/kubric?tab=readme-ov-file

Data Modality: Image, Depth, Camera extrinsics and intrinsics.

Image Training Data Size [Less than a Million Images]

Data Collection Method by dataset [Synthetic]

Labeling Method by dataset [Synthetic]

Properties: A data generation pipeline using PyBullet and Blender to create photo-realistic synthetic videos with rich annotations. We generate a synthetic dataset using based on assets from Google Scanned Objects, environments from HDRI Haven

Wild RGB-D

Link: https://github.com/wildrgbd/wildrgbd

Data Modality: Image, Depth, Camera extrinsics and intrinsics.

Image Training Data Size [1 Million to 1 Billion Images]

Data Collection Method by dataset [Human]

Labeling Method by dataset [Automatic/Sensors]

Properties: Large-scale dataset comprising ~20,000 RGB-D videos of objects captured in the wild using iPhones. Includes camera poses and object masks.

BlendedMVS

Link: https://github.com/YoYo000/BlendedMVS

Data Modality: Image, Depth, Camera extrinsics and intrinsics.

Image Training Data Size [Less than a Million Images]

Data Collection Method by dataset [Synthetic]

Labeling Method by dataset [Synthetic]

Properties: Multi-view stereo dataset. 3D models are reconstructed from real images, then rendered to create synthetic training pairs blended with the original images.

DL3DV-10K

Link: https://dl3dv-10k.github.io/DL3DV-10K/

Data Modality: Image, Camera extrinsics and intrinsics.

Image Training Data Size [1 Million to 1 Billion Images]

Data Collection Method by dataset [Human]

Labeling Method by dataset [Automatic/Sensors]

Properties: Large-scale scene dataset containing over 10,000 videos of diverse real-world scenes captured for Deep Learning based 3D Vision. Camera poses are obtained using structure-from-motion.

Spring Benchmark

Link: https://spring-benchmark.org/

Data Modality: Image, depth, Camera extrinsics and intrinsics.

Image Training Data Size [Less than a Million Images]

Data Collection Method by dataset [Synthetic]

Labeling Method by dataset [Synthetic]

Properties: High-resolution photo-realistic computer-generated imagery (Blender) for optical flow and stereo benchmarking.

TartanAir

Link: https://theairlab.org/tartanair-dataset/

Data Modality: Image, Depth, Camera extrinsics and intrinsics.

Image Training Data Size [1 Million to 1 Billion Images]

Data Collection Method by dataset [Synthetic]

Labeling Method by dataset [Synthetic]

Properties: Challenging visual SLAM dataset collected in photorealistic simulation environments with various lighting/weather conditions.

UnrealStereo4K

Link: https://github.com/fabiotosi92/SMD-Nets#unrealstereo4k

Data Modality: Image, Depth, Camera extrinsics and intrinsics.

Image Training Data Size [Less than a Million Images]

Data Collection Method by dataset [Synthetic]

Labeling Method by dataset [Synthetic]

Properties: High-resolution synthetic stereo dataset generated using Unreal Engine 4.

Evaluation Datasets

7scenes

Link: 7scenes

Data Modality: Image, depth, Camera extrinsics and intrinsics.

Image Training Data Size [Less than a Million Images]

Data Collection Method by dataset [Human]

Labeling Method by dataset [Automatic/Sensors]

Properties: 7-Scenes is a RGB-D dataset featuring seven indoor scenes captured with a Kinect camera. The dataset includes RGB images (640x480), depth maps, and camera-to-world poses.

DTU MVS Dataset

Link: https://roboimagedata.compute.dtu.dk/?page_id=36

Data Modality: Image, depth, Camera extrinsics and intrinsics.

Image Training Data Size [Less than a Million Images]

Data Collection Method by dataset [Human]

Labeling Method by dataset [Automatic/Sensors]

Properties: An industrial robot arm was mounted with a structured light scanner. This allowed for structured light scans corresponding to each image in the data set. The images were taken by one of the cameras in the structured light scanner.

ETH3D Stereo Benchmark

Link: https://www.eth3d.net/overview

Data Modality: Image, depth, Camera extrinsics and intrinsics.

Image Training Data Size [1 Million to 1 Billion Images]

Data Collection Method by dataset [Human]

Labeling Method by dataset [Automatic/Sensors]

Properties: The multi-view stereo / 3D reconstruction benchmark covers a variety of indoor and outdoor scenes. Ground truth geometry has been obtained using a high-precision laser scanner. A DSLR camera as well as a synchronized multi-camera rig with varying field-of-view was used to capture images.

ScanNet

Link: http://www.scan-net.org/

Data Modality: Image, depth, Camera extrinsics and intrinsics.

Image Training Data Size [Less than a Million Images]

Data Collection Method by dataset [Human]

Labeling Method by dataset [Hybrid: Automatic/Sensors, Human]

Properties: ScanNet is an RGB-D video dataset containing 2.5 million views in more than 1500 scans, annotated with 3D camera poses, surface reconstructions, and instance-level semantic segmentations. To collect this data, we designed an easy-to-use and scalable RGB-D capture system that includes automated surface reconstruction and crowdsourced semantic annotation.

Sintel

Link: http://sintel.is.tue.mpg.de/

Data Modality: Image, depth, Camera extrinsics and intrinsics.

Image Training Data Size [Less than a Million Images]

Data Collection Method by dataset [Synthetic]

Labeling Method by dataset [Synthetic]

Properties: 1,064 frames organized into 23 video sequences. The data is Computer-Generated Imagery (CGI) derived from the open-source 3D animated short film Sintel.

TUM RGB-D SLAM dataset

Link: https://cvg.cit.tum.de/data/datasets/rgbd-dataset

Data Modality: Image, depth, Camera extrinsics and intrinsics.

Image Training Data Size [Less than a Million Images]

Data Collection Method by dataset [Human]

Labeling Method by dataset [Automatic/Sensors]

Properties: Large dataset containing RGB-D data and ground-truth data with the goal to establish a novel benchmark for the evaluation of visual odometry and visual SLAM systems. The dataset contains the color and depth images of a Microsoft Kinect sensor along the ground-truth trajectory of the sensor. The data was recorded at full frame rate (30 Hz) and sensor resolution (640x480). The ground-truth trajectory was obtained from a high-accuracy motion-capture system with eight high-speed tracking cameras (100 Hz).

Bonn RGB-D Dynamic Dataset

Link: https://www.ipb.uni-bonn.de/data/rgbd-dynamic-dataset/index.html

Data Modality: Image, depth, Camera extrinsics and intrinsics.

Image Training Data Size [Less than a Million Images]

Data Collection Method by dataset [Human]

Labeling Method by dataset [Automatic/Sensors]

Properties: This is a dataset for RGB-D SLAM, containing dynamic sequences. It provides 24 dynamic sequences, where people perform different tasks, such as manipulating boxes or playing with balloons, plus 2 static sequences. For each scene we provide the ground truth pose of the sensor, recorded with an Optitrack Prime 13 motion capture system. The sequences are in the same format as the TUM RGB-D Dataset, so that the same evaluation tools can be used. Furthermore, they provide a ground truth 3D point cloud of the static environment recorded using a Leica BLK360 terrestrial laser scanner.

KITTI Vision Benchmark Suite

Link: https://www.cvlibs.net/datasets/kitti/

Data Modality: Image, depth, Camera extrinsics and intrinsics.

Image Training Data Size [Less than a Million Images]

Data Collection Method by dataset [Human]

Labeling Method by dataset [Hybrid: Automatic/Sensors, Human]

Properties: Challenging real-world computer vision benchmarks. Tasks of interest are: stereo, optical flow, visual odometry, 3D object detection and 3D tracking. For this purpose, they equipped a standard station wagon with two high-resolution color and grayscale video cameras. Accurate ground truth is provided by a Velodyne laser scanner and a GPS localization system. The datasets are captured by driving around the mid-size city of Karlsruhe, in rural areas and on highways. Up to 15 cars and 30 pedestrians are visible per image.

Wayspots

Link: https://nianticlabs.github.io/ace/wayspots.html

Data Modality: Image, depth, Camera extrinsics and intrinsics.

Image Training Data Size [Less than a Million Images]

Data Collection Method by dataset [Human]

Labeling Method by dataset [Automatic/Sensors]

Properties: 10 small outdoor scenes stemming from Niantic's Map-Free dataset. Each scene was scanned twice, independently by two users. Posed mapping images come from real-time visual odometry on the user's phones. Depth or 3D point clouds are not available. Evaluation ground truth comes from SfM.

Inference

Acceleration Engine: N/A Test Hardware: NVIDIA A100

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

Citation

If you find this work useful, please cite:

@inproceedings{elflein2026vggttt,
  title     = {VGG-T\textsuperscript{3}: Offline Feed-Forward 3D Reconstruction at Scale},
  author    = {Elflein, Sven and Li, Ruilong and Agostinho, S{\'e}rgio and Gojcic, Zan and Leal-Taix{\'e}, Laura and Zhou, Qunjie and Osep, Aljosa},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2026}
}

Task: Image-to-3D
Library: vggttt
License: other
Created: Dec 10, 2025
Updated: May 30, 2026