Model Card - Fast-FoundationStereo

Model Overview

Description:

Fast-FoundationStereo estimates the disparity of each pixel in a rectified binocular stereo pair of images. It is a transformer-based foundational model that shows strong zero-shot generalization while running in real time. Fast-FoundationStereo was developed by NVIDIA. This model is ready for commercial or non-commercial use.

License/Terms of Use:

This model is released under the NVIDIA Open Model Agreement: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/

Deployment Geography:

Global

Use Case:

Researchers and developers in the field of computer vision, specifically those interested in depth estimation, are expected to use this method for tasks such as three-dimensional reconstruction, object detection, object pose estimation, and scene understanding.

Release Date:

Github 02/01/2026 via https://github.com/NVlabs/Fast-FoundationStereo

References(s):

Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matching

Model Architecture:

Architecture Type: Transformer and Convolutional Neural Network (CNN)

Network Architecture: The network contains three parts: 1) an EdgeNeXt student module that distills the original FoundationStereo feature extractor; 2) a set of CNN and transformer blocks that perform matching with long-range dependencies; 3) a reduced set of convGRU blocks.

This model was developed based on FoundationStereo.

Number of model parameters: 14.6M (1.46*10^7).

Computational Load (Internal Only: For NVIDIA Models Only)

Cumulative Compute: Trained on 32 × NVIDIA A100-SXM4-80GB GPUs (4 nodes × 8 GPUs) for 206,389 s of wall-clock time (~57.3 h, ~1.83 × 10^3 GPU-hours) using FP16 mixed-precision. At the A100 dense FP16 Tensor Core peak of 312 TFLOPS, this corresponds to an upper-bound cumulative compute of ~2.06 × 10^21 FLOPs.
\nEstimated Energy and Emissions for Model Training: Using the A100-SXM4-80GB TDP of 400 W, GPU energy ≈ 734 kWh. Applying a 1.5× node-level overhead (CPU, DRAM, NIC, storage; Patterson et al. 2021) and a 1.2 data-center PUE yields ≈ 1.32 × 10^3 kWh of total system energy. Assuming a grid carbon intensity of 0.37 kg CO₂eq/kWh (U.S. average), estimated emissions ≈ 4.9 × 10^2 kg CO₂eq.

Input(s):

Input Type(s): Image (a pair of two rectified binocular stereo images)

Input Format(s):

Image: Red, Green, Blue (RGB)

Input Parameters:

Image: Two-Dimensional (2D)

Other Properties Related to Input: Rectified stereo image pair from a camera such as Zed. The baseline is needed to convert disparity to depth. No alpha channel and no pre-processing needed. Bit depth: 24-bit.

Output(s)

Output Type(s): Image (disparity map)

Output Format(s):

Image: 16-bit unsigned integer

Output Parameters:

Image: Two-Dimensional (2D)

Other Properties Related to Output: Final 2D disparity map. No alpha channel and no post-processing needed. Bit depth: 16-bit.

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):

Not Applicable (N/A)

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere

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 model version with full capabilities, unpruned and trained.

Training, Testing, and Evaluation Datasets:

Training Dataset:

Data Modality:

Image

Training Data Size:

** Image Training Data Size

1 Million to 1 Billion Images

** Data Collection Method by dataset

Hybrid: Synthetic, Automatic/Sensors

** Labeling Method by dataset

Hybrid: Synthetic, Automatic/Sensors

Properties: The training dataset includes: 1) a large-scale internal proprietary synthetic dataset featuring 1.4 million stereo pairs with a large diversity of objects and scenes and high photorealism; 2) the external commercial SDG.

Link: Internal, proprietary dataset, and external commercial SDG.

Testing Dataset:

Link: Middlebury dataset.

Data Collection Method by dataset:

Automatic/Sensors

Labeling Method by dataset:

Automatic/Sensors

Properties: The dataset encompasses a wide range of scenarios, includes diverse three-dimensional assets, captures stereo images under diversely randomized camera parameters, and achieves high fidelity in both rendering and spatial layouts.

Evaluation Dataset:

Benchmark Score: Evaluated on the public leaderboards well known to the stereo community.

Middlebury: https://vision.middlebury.edu/stereo/ — the classic leaderboard for dense stereo matching, developed at Middlebury College.
ETH3D: https://www.eth3d.net/ — a popular leaderboard for stereo, developed at ETH.
KITTI: https://www.cvlibs.net/datasets/kitti/eval_stereo.php — a cornerstone benchmark for autonomous driving research.

Data Collection Method by dataset:

Automatic/Sensors

Labeling Method by dataset:

Automatic/Sensors

Properties:

The Middlebury Stereo dataset consists of high-resolution stereo sequences with complex geometry and pixel-accurate ground-truth disparity data. The ground-truth disparities were acquired using a novel technique that employs structured lighting and infrared paint.
ETH3D is a multi-view stereo / 3D reconstruction benchmark that covers a variety of indoor and outdoor scenes. Ground-truth geometry was 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.
KITTI stereo dataset is a cornerstone of autonomous driving research, developed by the Karlsruhe Institute of Technology (KIT) and the Toyota Technological Institute at Chicago (TTIC). It provides real-world, high-resolution stereo imagery paired with precise ground-truth depth data collected from a moving vehicle in diverse urban environments.

Inference:

Acceleration Engine: TensorRT
\nTest Hardware:

Zed Stereo Camera
NVIDIA GeForce RTX 3090

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 make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.

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

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Model Card - Fast-FoundationStereo

\n\n# Model Overview\n\n### Description:\nFast-FoundationStereo estimates the disparity of each pixel in a rectified binocular stereo pair of images. It is a transformer-based foundational model that shows strong zero-shot generalization while running in real time. Fast-FoundationStereo was developed by NVIDIA. This model is ready for commercial or non-commercial use.
\n\n### License/Terms of Use:\nThis model is released under the NVIDIA Open Model Agreement: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/
\n\n### Deployment Geography:\nGlobal
\n\n### Use Case:
\nResearchers and developers in the field of computer vision, specifically those interested in depth estimation, are expected to use this method for tasks such as three-dimensional reconstruction, object detection, object pose estimation, and scene understanding.
\n\n### Release Date:
\nGithub 02/01/2026 via https://github.com/NVlabs/Fast-FoundationStereo
\n\n## References(s):\n[Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matching](https://arxiv.org/abs/2512.11130)
\n\n## Model Architecture:\n**Architecture Type:** Transformer and Convolutional Neural Network (CNN)
\n\n**Network Architecture:** The network contains three parts: 1) an EdgeNeXt student module that distills the original FoundationStereo feature extractor; 2) a set of CNN and transformer blocks that perform matching with long-range dependencies; 3) a reduced set of convGRU blocks.
\n\n**This model was developed based on FoundationStereo.**
\n\n**Number of model parameters:** 14.6M (1.46*10^7).
\n\n## Computational Load (Internal Only: For NVIDIA Models Only)\n**Cumulative Compute:** Trained on 32 × NVIDIA A100-SXM4-80GB GPUs (4 nodes × 8 GPUs) for 206,389 s of wall-clock time (~57.3 h, ~1.83 × 10^3 GPU-hours) using FP16 mixed-precision. At the A100 dense FP16 Tensor Core peak of 312 TFLOPS, this corresponds to an upper-bound cumulative compute of ~2.06 × 10^21 FLOPs.
\n**Estimated Energy and Emissions for Model Training:** Using the A100-SXM4-80GB TDP of 400 W, GPU energy ≈ 734 kWh. Applying a 1.5× node-level overhead (CPU, DRAM, NIC, storage; Patterson et al. 2021) and a 1.2 data-center PUE yields ≈ 1.32 × 10^3 kWh of total system energy. Assuming a grid carbon intensity of 0.37 kg CO₂eq/kWh (U.S. average), estimated emissions ≈ 4.9 × 10^2 kg CO₂eq.
\n\n## Input(s):
\n**Input Type(s):** Image (a pair of two rectified binocular stereo images)
\n\n**Input Format(s):**\n- Image: Red, Green, Blue (RGB)
\n\n**Input Parameters:**\n- Image: Two-Dimensional (2D)
\n\n**Other Properties Related to Input:** Rectified stereo image pair from a camera such as Zed. The baseline is needed to convert disparity to depth. No alpha channel and no pre-processing needed. Bit depth: 24-bit.
\n\n## Output(s)\n\n**Output Type(s):** Image (disparity map)
\n\n**Output Format(s):**\n- Image: 16-bit unsigned integer
\n\n**Output Parameters:**\n- Image: Two-Dimensional (2D)
\n\n**Other Properties Related to Output:** Final 2D disparity map. No alpha channel and no post-processing needed. Bit depth: 16-bit.
\n\nOur 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.
\n\n## Software Integration:\n**Runtime Engine(s):**\n* Not Applicable (N/A)
\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Ampere
\n\n**Supported Operating System(s):**\n* Linux
\n\nThe 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.
\n\n## Model Version(s):\nv1.0: Initial model version with full capabilities, unpruned and trained.
\n\n## Training, Testing, and Evaluation Datasets:\n\n## Training Dataset:\n\n### Data Modality:\n* Image
\n\n### Training Data Size:\n\n** Image Training Data Size
\n* 1 Million to 1 Billion Images
\n\n** Data Collection Method by dataset
\n* Hybrid: Synthetic, Automatic/Sensors
\n\n** Labeling Method by dataset
\n* Hybrid: Synthetic, Automatic/Sensors
\n\n**Properties:** The training dataset includes: 1) a large-scale internal proprietary synthetic dataset featuring 1.4 million stereo pairs with a large diversity of objects and scenes and high photorealism; 2) the external commercial SDG.\n\n**Link:** Internal, proprietary dataset, and external commercial SDG.
\n\n### Testing Dataset:\n\n**Link:** Middlebury dataset.
\n\nData Collection Method by dataset:
\n* Automatic/Sensors
\n\nLabeling Method by dataset:
\n* Automatic/Sensors
\n\n**Properties:** The dataset encompasses a wide range of scenarios, includes diverse three-dimensional assets, captures stereo images under diversely randomized camera parameters, and achieves high fidelity in both rendering and spatial layouts.
\n\n### Evaluation Dataset:\n\n**Benchmark Score:** Evaluated on the public leaderboards well known to the stereo community.
\n\n* Middlebury: https://vision.middlebury.edu/stereo/ — the classic leaderboard for dense stereo matching, developed at Middlebury College.
\n* ETH3D: https://www.eth3d.net/ — a popular leaderboard for stereo, developed at ETH.
\n* KITTI: https://www.cvlibs.net/datasets/kitti/eval_stereo.php — a cornerstone benchmark for autonomous driving research.
\n\nData Collection Method by dataset:
\n* Automatic/Sensors
\n\nLabeling Method by dataset:
\n* Automatic/Sensors
\n\n**Properties:**\n* The Middlebury Stereo dataset consists of high-resolution stereo sequences with complex geometry and pixel-accurate ground-truth disparity data. The ground-truth disparities were acquired using a novel technique that employs structured lighting and infrared paint.
\n* ETH3D is a multi-view stereo / 3D reconstruction benchmark that covers a variety of indoor and outdoor scenes. Ground-truth geometry was 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.
\n* KITTI stereo dataset is a cornerstone of autonomous driving research, developed by the Karlsruhe Institute of Technology (KIT) and the Toyota Technological Institute at Chicago (TTIC). It provides real-world, high-resolution stereo imagery paired with precise ground-truth depth data collected from a moving vehicle in diverse urban environments.
\n\n## Inference:\n**Acceleration Engine:** TensorRT
\n**Test Hardware:**
\n* Zed Stereo Camera
\n* NVIDIA GeForce RTX 3090
\n\n## Ethical Considerations:\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\nPlease make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.
\n\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
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Governing Terms: Your use of the model is governed by the NVIDIA Open Model Agreement.

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These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624],[569,570,571,572,573],{"_372":464,"_208":464,"_179":218,"_345":595},{"_372":430,"_208":430,"_179":218,"_345":590},{"_372":583,"_208":583,"_179":218,"_345":584},{"_372":579,"_208":579,"_179":218,"_345":580},{"_372":574,"_208":574,"_179":218,"_345":575},"nvcrm.my.site.com",[576],{"_78":577,"_378":574,"_379":34,"_179":578},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[581],{"_78":582,"_378":579,"_379":34,"_179":413},"edge","github.com",[585,586],{"_78":589,"_378":583,"_379":34,"_179":588},{"_78":587,"_378":583,"_379":34,"_179":588},"logged_in","This domain is owned by Github. 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a one year lifespan and contains no personal information.","default","

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