FoundationStereo Overview

Description

FoundationStereo is a foundation model developed by NVIDIA Research for Stereo Depth Estimation. The model takes as input an RGB stereo-image pair and outputs an accurate disparity map.\nThe model was designed to achieve strong zero-shot generalization and has been shown to generalize to various scenarios with wide zero-shot coverage.

Use Case:

FoundationStereo is designed for developers who intend to apply accurate zero-shot depth to 3D perception use cases in industrial, robotics, and smart space applications using stereo images as input.

This model is ready for commercial use.

License

License to use these models is covered by the NVIDIA Open Model License. By downloading the model, you accept the terms and conditions of these licenses.

Deployment Geography:

Global

Release Dates:

NGC 11/19/2025 via https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/foundationstereo
NGC 07/30/2025 via https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/foundationstereo
NGC 10/07/2025 via https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/foundationstereo

References

Wen, B., Trepte, M., Aribido, J., Kautz, J., Gallo, O., & Birchfield, S. (2025). FoundationStereo: Zero-Shot Stereo Matching. arXiv preprint arXiv:2501.09898.

Model Architecture

Architecture Type: Mixed Transformer-CNN based Network Architecture

Network Architecture:

The network consists of various modules. A generic designed feature extractor based on:

Pretrained DepthAnythingV2 and
A Side-tuning CNN based on pretrained EdgeNeXt features

The pretrained DepthAnythingV2 is a foundational monocular depth estimation network. The model is frozen in the feature extraction phase and its features are sandwiched with a pretrained CNN-based model, EdgeNeXt. The Edgenext model, though pretrained on NV-Imagenet, is not frozen during training to enable a side-tuning effect into its layer weights.

The extracted stereo features are passed into an Attentive Hybrid Cost Filtering (AHCF) cost volume. The cost volume computes rich correlated mappings between stereo features.

In addition, a disparity transformer is also used to compute feature attention. The features from the disparity transformer are combined with the cost features from the cost volume and combined to estimate an initial disparity map.

To obtain highly accurate disparity output, the initial estimated disparity maps are refined iteratively using a convolutional GRU. Lastly, the mean average error metric is used to assess the model performance.

Number of model parameters: 6.3 * 10^7

Input

Input Types: Two RGB Stereo Images
Input Formats: RGB image: Red, Green, Blue (RGB), Grayscale image.
Input Parameters: Two-Dimensional (2D)
Other Properties Related to Input: B X 3 X H X W (Batch Size x Channel x Height x Width)

Output

Output Types: Image.
Output Format: Disparity Map.
Output Parameters: Two-Dimensional (2D)
Other Properties Related to Output: B x 3 x H x W (Batch Size x Channel x Height x Width)

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware and software frameworks, FoundationStereo achieves faster training and inference times compared to CPU-only solutions.

Software Integration

Runtime:

FoundationStereo perception module supports PyTorch inference, ONNX and Tensorrt Runtimes.

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Blackwell
NVIDIA Jetson
NVIDIA Hopper
NVIDIA Lovelace
NVIDIA Pascal
NVIDIA Turing
NVIDIA Volta

[Preferred/Supported] Operating Systems:

Linux

Model Version(s):

\n
Deployable_foundation_stereo_small_v1.0: contains two zipped decrypted ONNX files, inferencable on TensorrtEngine and ONNX runtime.
\n
- deployable_foundationstereo_small_576x960_v1.0.onnx : supports fixed inputs with spatial resolution of: 576x960
- deployable_foundationstereo_small_320x736_v1.0.onnx : supports fixed inputs with spatial resolution of: 320x736
\n
\n
deployable_foundation_stereo_small_dynamic_v1.0: decrypted ONNX file, inferencable on only ONNX runtime.
\n

Training and Evaluation Datasets

The network is trained using a mixture of various datasets -synthetically generated data and real-world collected datasets with pseudo-label groundtruth.

During training, the model predicts a coarse initial disparity. A GRU module uses a context network pipeline to refine the initial disparity using a specified iteration sequence. L1-loss is applied to the refined disparity and the network weights are updated accordingly. The validation dataset is carefully selected from a mix of synthetic and real data.

Training Dataset

The synthetic dataset used consists of three variants of datasets namely: Foundation Stereo Dataset (FSD), Crestereo and Tartanair. The training datasets can be downloaded at the links below.

The model is first pretrained on FSD and Crestereo, and then finetuned on the former and Tartanair.

Link

Foundation Stereo Synthetic Dataset: Generated by NVIDIA Omniverse.
Crestero Dataset
Tartanair Dataset

Data Collection Method by dataset\nHybrid: Synthetic, Human.

The synthetic dataset used was generated using NVIDIA Omniverse and NVIDIA native 3D assets.\nThe real dataset collection was driven by NVIDIA using well-regulated, consent driven approaches.

Labeling Method by dataset

We used only synthetic (SDG) dataset for training, which already contains pseudo-labelled synthetic depth labels.

Properties:

SDG:
FSD Size: 1.6 M
Resolution: 720 x 1280 x 3
Crestereo Size: 200 K
Resolution: 1080 x 1920
Tartanair: 307k
Resolution: 480 x 640

Evaluation Dataset

Link

Data Collection Method by Dataset

FSD Dataset: Synthetic
Crestereo Dataset: Synthetic
Tartanair: Synthetic

Labeling Method by Dataset

FSD Dataset: Synthetic
Crestereo Dataset: Synthetic
Tartanair: Synthetic

Inference

Acceleration Engine\nTensor(RT)

Test Hardware:

H100
A100
RTX 5090
L40
Thor
AGX Orin

The inference performance of the FoundationStereo model is evaluated at FP16 precision. In this model card, we included 2 fixed input shape models with spatial resolutions: 320x736 and 576x960 in zipped file respectively. We also include one dynamic-shape model that could take arbitrary input resolutions. However, the dynamic model could not be converted to TensorrtEngine in FP16 precision. Hence, user can only run ONNX inference with the dynamic model.

Both fixed shaped models can be run on ONNX and TRT runtimes. The performance assessment was conducted using trtexec on a range of devices. In the table below, we specify various inference rates on diverse hardware platforms. In the table, \"BS\" stands for \"batch size.\"

The performance data presented pertains solely to model inference. The end-to-end performance, when integrated with streaming video data, pre-processing and post-processing, might differ due to potential bottlenecks in hardware and software.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Models (FP16)	Devices	Resolution	Latency (BS=1)	Throughput per Second (BS=1)
FoundationStereo	Orin AGX	320x736	1039.00	1.00
FoundationStereo	Thor AGX	320x736	1460.95	0.67
FoundationStereo	Thor AGX	576x960	2750.00	0.36
FoundationStereo	A100	320x736	199.32	5.00
FoundationStereo	A100	576x960	437.14	2.24

Output Image

Limitations

Failure Cases

FoundationStereo may produce unreliable depth estimates of transparent objects (e.g., glass and water), high-saturation scenes, or poorly lit areas.

Inference Method

These models are designed for use with NVIDIA hardware and software. For hardware, the models are compatible with any NVIDIA GPU, including NVIDIA Jetson devices. For software, the models are specifically designed for the TensorRT.

The primary application of this model is to estimate an object's depth from a stereo RGB pair.

The model is designed for deployment on edge devices using TensorRT. TAO Triton apps offer capabilities to construct efficient image analytic pipelines. These pipelines can capture, decode, and process data before executing inference.

Instructions to Deploy the Model with Triton Inference Server

To create the entire end-to-end inference application, deploy this model with [Triton Inference Server] (https://developer.nvidia.com/nvidia-triton-inference-server). NVIDIA Triton Inference Server is an open-source inference serving software that helps standardize model deployment and execution and delivers fast and scalable AI in production. Triton supports direct integration of this model into the server and inference from a client.

To deploy this model with [Triton Inference Server] (https://developer.nvidia.com/nvidia-triton-inference-server) and end-to-end inference from images, please refer to the TAO Triton apps.

Limitations:

The training and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies.

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.

For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

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The model takes as input an RGB stereo-image pair and outputs an accurate disparity map.\nThe model was designed to achieve strong zero-shot generalization and has been shown to generalize to various scenarios with wide zero-shot coverage. \n\n#### Use Case:\nFoundationStereo is designed for developers who intend to apply accurate zero-shot depth to 3D perception use cases in industrial, robotics, and smart space applications using stereo images as input.\n\nThis model is ready for commercial use.\n\n### License \n\nLicense to use these models is covered by the NVIDIA Open Model License. By downloading the model, you accept the terms and conditions of these [licenses](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\n\n### Deployment Geography:\nGlobal\n\n### Release Dates:\n- NGC 11/19/2025 via https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/foundationstereo\n- NGC 07/30/2025 via https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/foundationstereo\n- NGC 10/07/2025 via https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/foundationstereo\n\n### References\nWen, B., Trepte, M., Aribido, J., Kautz, J., Gallo, O., & Birchfield, S. (2025). FoundationStereo: Zero-Shot Stereo Matching. arXiv preprint arXiv:2501.09898.\n\n\n### Model Architecture \n\n**Architecture Type:** Mixed Transformer-CNN based Network Architecture\n\n**Network Architecture:**\n\nThe network consists of various modules. A generic designed feature extractor based on:\n\n - Pretrained [DepthAnythingV2](https://arxiv.org/abs/2406.09414) and\n - A Side-tuning CNN based on pretrained [EdgeNeXt](https://arxiv.org/abs/2206.10589) features\n\nThe pretrained DepthAnythingV2 is a foundational monocular depth estimation network. The model is frozen in the feature extraction phase and its features are sandwiched with a pretrained CNN-based model, EdgeNeXt. The Edgenext model, though pretrained on NV-Imagenet, is not frozen during training to enable a side-tuning effect into its layer weights.\n\nThe extracted stereo features are passed into an Attentive Hybrid Cost Filtering (AHCF) cost volume. The cost volume computes rich correlated mappings between stereo features.\n\nIn addition, a disparity transformer is also used to compute feature attention. The features from the disparity transformer are combined with the cost features from the cost volume and combined to estimate an initial disparity map.\n\nTo obtain highly accurate disparity output, the initial estimated disparity maps are refined iteratively using a convolutional GRU. Lastly, the mean average error metric is used to assess the model performance.\n\n**Number of model parameters**: 6.3 * 10^7\n\n### Input\n\n- **Input Types**: Two RGB Stereo Images\n- **Input Formats**: RGB image: Red, Green, Blue (RGB), Grayscale image.\n- **Input Parameters**: Two-Dimensional (2D)\n- **Other Properties Related to Input**: B X 3 X H X W (Batch Size x Channel x Height x Width)\n\n\n### Output\n\n- **Output Types**: Image.\n- **Output Format**: Disparity Map.\n- **Output Parameters**: Two-Dimensional (2D)\n- **Other Properties Related to Output**: B x 3 x H x W (Batch Size x Channel x Height x Width) \n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware and software frameworks, FoundationStereo achieves faster training and inference times compared to CPU-only solutions.\n\n### Software Integration \n\n**Runtime**:\n\nFoundationStereo perception module supports PyTorch inference, ONNX and Tensorrt Runtimes.\n\n**Supported Hardware Microarchitecture Compatibility**:\n\n- NVIDIA Ampere\n- NVIDIA Blackwell\n- NVIDIA Jetson\n- NVIDIA Hopper\n- NVIDIA Lovelace\n- NVIDIA Pascal\n- NVIDIA Turing\n- NVIDIA Volta\n\n**[Preferred/Supported] Operating Systems**:\n\n- Linux\n\n### Model Version(s):\n\n- **Deployable_foundation_stereo_small_v1.0**: contains two zipped decrypted [ONNX](https://onnx.ai/) files, inferencable on TensorrtEngine and ONNX runtime.\n - deployable_foundationstereo_small_576x960_v1.0.onnx : supports fixed inputs with spatial resolution of: 576x960\n - deployable_foundationstereo_small_320x736_v1.0.onnx : supports fixed inputs with spatial resolution of: 320x736\n\n- **deployable_foundation_stereo_small_dynamic_v1.0**: decrypted [ONNX](https://onnx.ai/) file, inferencable on **only** ONNX runtime.\n\n## Training and Evaluation Datasets \n\nThe network is trained using a mixture of various datasets -synthetically generated data and real-world collected datasets with pseudo-label groundtruth.\n\nDuring training, the model predicts a coarse initial disparity. A GRU module uses a context network pipeline to refine the initial disparity using a specified iteration sequence. L1-loss is applied to the refined disparity and the network weights are updated accordingly. The validation dataset is carefully selected from a mix of synthetic and real data.\n\n### Training Dataset\n\n\nThe synthetic dataset used consists of three variants of datasets namely: Foundation Stereo Dataset (FSD), Crestereo and Tartanair. The training datasets can be downloaded at the links below.\n\nThe model is first pretrained on FSD and Crestereo, and then finetuned on the former and Tartanair.\n\n**Link**\n\n- [Foundation Stereo Synthetic Dataset](https://drive.google.com/drive/folders/1YdC2a0_KTZ9xix_HyqNMPCrClpm0-XFU?usp=sharing): Generated by NVIDIA Omniverse.\n- [Crestero Dataset](https://github.com/megvii-research/CREStereo?tab=readme-ov-file)\n- [Tartanair Dataset](https://tartanair.org/)\n\n**Data Collection Method by dataset**\nHybrid: Synthetic, Human.\n\nThe synthetic dataset used was generated using NVIDIA Omniverse and NVIDIA native 3D assets.\nThe real dataset collection was driven by NVIDIA using well-regulated, consent driven approaches.\n\n\n**Labeling Method by dataset**\n\nWe used only synthetic (SDG) dataset for training, which already contains pseudo-labelled synthetic depth labels.\n\n\n**Properties**:\n- SDG:\n - FSD Size: 1.6 M\n - Resolution: 720 x 1280 x 3\n - Crestereo Size: 200 K\n - Resolution: 1080 x 1920\n - Tartanair: 307k\n - Resolution: 480 x 640\n\n### Evaluation Dataset\n\n**Link**\n- [Middlebury Dataset](https://vision.middlebury.edu/stereo/data/)\n- [Eth3D Dataset](https://www.eth3d.net/datasets)\n- [Kitti Dataset](https://www.cvlibs.net/datasets/kitti/)\n\n**Data Collection Method by Dataset**\n\n- FSD Dataset: Synthetic\n- Crestereo Dataset: Synthetic\n- Tartanair: Synthetic\n\n**Labeling Method by Dataset**\n\n- FSD Dataset: Synthetic\n- Crestereo Dataset: Synthetic\n- Tartanair: Synthetic\n\n\n#### Inference\n\n**Acceleration Engine**\nTensor(RT)\n\n**Test Hardware**:\n\n- H100\n- A100\n- RTX 5090\n- L40\n- Thor\n- AGX Orin\n\nThe inference performance of the FoundationStereo model is evaluated at FP16 precision. In this model card, we included 2 fixed input shape models with spatial resolutions: 320x736 and 576x960 in zipped file respectively. We also include one dynamic-shape model that could take arbitrary input resolutions. However, the dynamic model could not be converted to TensorrtEngine in FP16 precision. Hence, user can only run ONNX inference with the dynamic model. \n\nBoth fixed shaped models can be run on ONNX and TRT runtimes. The performance assessment was conducted using **trtexec** on a range of devices. In the table below, we specify various inference rates on diverse hardware platforms. In the table, \"BS\" stands for \"batch size.\"\n\nThe performance data presented pertains solely to model inference. The end-to-end performance, when integrated with streaming video data, pre-processing and post-processing, might differ due to potential bottlenecks in hardware and software.\n\n| Models (FP16) | Devices | Resolution | Latency (BS=1)| Throughput per Second (BS=1) |\n| ---- | ---- | ---- | ---- | ---- |\n| FoundationStereo | Orin AGX | 320x736 | 1039.00 | 1.00 | \n| FoundationStereo | Thor AGX | 320x736 | 1460.95 | 0.67 |\n| FoundationStereo | Thor AGX | 576x960 | 2750.00 | 0.36 |\n| FoundationStereo | A100 | 320x736 | 199.32 | 5.00 |\n| FoundationStereo | A100 | 576x960 | 437.14 | 2.24 |\n\n#### Output Image\n\n

\n\n\n### Limitations \n\n#### Failure Cases\n\nFoundationStereo may produce unreliable depth estimates of transparent objects (e.g., glass and water), high-saturation scenes, or poorly lit areas.\n\n### Inference Method\n\nThese models are designed for use with NVIDIA hardware and software. For hardware, the models are compatible with any NVIDIA GPU, including NVIDIA Jetson devices. For software, the models are specifically designed for the [TensorRT](https://developer.nvidia.com/tensorrt).\n\nThe primary application of this model is to estimate an object's depth from a stereo RGB pair.\n\nThe model is designed for deployment on edge devices using TensorRT. [TAO Triton apps](https://github.com/NVIDIA-AI-IOT/tao-toolkit-triton-apps) offer capabilities to construct efficient image analytic pipelines. These pipelines can capture, decode, and process data before executing inference.\n\n#### Instructions to Deploy the Model with Triton Inference Server\n\nTo create the entire end-to-end inference application, deploy this model with [Triton Inference Server] (https://developer.nvidia.com/nvidia-triton-inference-server). NVIDIA Triton Inference Server is an open-source inference serving software that helps standardize model deployment and execution and delivers fast and scalable AI in production. Triton supports direct integration of this model into the server and inference from a client.\n\nTo deploy this model with [Triton Inference Server] (https://developer.nvidia.com/nvidia-triton-inference-server) and end-to-end inference from images, please refer to the [TAO Triton apps](https://github.com/NVIDIA-AI-IOT/tao-toolkit-triton-apps).\n\n### Limitations:\nThe training and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies.\n\n### Ethical Considerations\n\nNVIDIA 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.\n\nFor more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.\n\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","FoundationStereo",["D",1763760465321],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/foundationstereo","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",1939651792,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","| Field | Response |\n| -- | -- |\n|Participation considerations from adversely impacted groups [(protected classes)](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None of the Above |\n| Measures taken to mitigate against unwanted bias: | None.|","explainability","| Field | Response |\n| -- | -- |\n| Intended Application(s) & Domain(s): | Depth Perception for Robotics, Autonomous Vision Navigation and Space Mapping |\n| Model Type: | Zero-shot Depth Estimation |\n| Intended Users: | This model is intended for uses who need accurate 3D depth for 2D Stereo Vision |\n| Output: | Disparity or Depth Map |\n| Describe how the model works: | 1. Pass in two Stereo RGB images. 2. Run the model on the stereo input. 3. Save the disparity map and use camera intrinsic valus to obtain the corresponding depth map. |\n| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of | Not Applicable |\n| Technical Limitations: | This model may not work well for detecting partially or fully occluded objects. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Percentage of bad pixels, End-point Error (EPE) |\n| Potential Known Risks | Poor depth perception from a failure case could lead to hallucination of objects into the depth scene or wrong depth estimation of objects in a scene |\n| Licensing: | [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) |","privacy","| Field | Response |\n| -- | -- |\n| Generatable or reverse engineerable personal data? | No |\n| Personal data used to create this model? | No|\n| How often is dataset reviewed? | Before Release |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Applicable NVIDIA Privacy Policy\t| [Applicable Privacy Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) |","safetyAndSecurity","| Field | Response |\n| -- | -- |\n| Model Application(s): | Depth Perception Model for Robotics and Autonomous Driving Applications |\n| Describe the life-critical application (if present). | None |\n| Use Case Restrictions: | Abide by [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) |\n| Describe access restrictions (if any): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. 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