Holoscan Sample App Data for Point Tracking in Colonoscopy

This resource contains the TracksTo4D model for 4D point tracking in colonoscopy videos, and a sample surgical video.

\n
Kasten, Y., Lu, W., & Maron, H. (2024). Fast encoder-based 3d from casual videos via point track processing. Advances in Neural Information Processing Systems, 37, 96150-96180.
\n

Model

Description:

A real-time, unsupervised method for generating dynamic 4D point clouds and accurate camera tracking from colonoscopy videos, with 27x better camera pose and 1.6mm depth accuracy. Since it is unsupervised, it eliminates the need to obtain ground-truth 3D data in clinical settings. This model is ready for commercial use.

For an application of this model in Holoscan SDK, please see Holohub Link.

License/Terms of Use:

NVIDIA Open Model License

Deployment Geography:

Global

Use Case:

Clinical colonoscopy procedures for real-time 3D reconstruction and camera tracking.

References(s):

TracksTo4D: Kasten, Y., Lu, W., & Maron, H. (2024). Fast encoder-based 3d from casual videos via point track processing. Advances in Neural Information Processing Systems, 37, 96150-96180.

Model Architecture:

Architecture Type: Transformer
Network Architecture: TracksTo4D
Number of model parameters: 12M

Input(s):

Input Type(s): 2D Point Tracking from Colonoscopy Videos
Input Format(s): Tensor
Input Parameters: Two-Dimensional (2D)
Other Properties Related to Input: The model takes 2D feature tracks from colonoscopy videos as input. Tracking points projected to the screen space.

Output(s)

Output Type(s): 4D Point Cloud, Camera Pose
Output Format(s): Tensor, Tensor
Output Parameters: Three-Dimensional (3D), Three-Dimensional (3D)
Other Properties Related to Output: Dynamic 4D point clouds and 6-DoF camera tracking.

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):

TensorRT

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Blackwell
NVIDIA Hopper

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.

This AI model can be used with Holoscan for optimal performance.

Model Version(s):

v1.0

Training, Testing, and Evaluation Datasets:

Dataset Overview

Total Size: 2.7 Million Frames\nTotal Number of Datasets: 1\nDataset partition: 100%

Given that the raw footage suffered from quality issues such as motion blur, fluid occlusion, and camera-wall collisions, we implemented a geometric curation pipeline based on Structure-from-Motion (SfM). We processed the data into 20-frame sequences, estimating camera poses and sparse 3D structure, followed by refinement via Bundle Adjustment and metric auto-calibration. Crucially, this pipeline handles the dynamic nature of the colon by isolating a subset of static points that satisfy rigid multiview constraints. Sequences were then automatically filtered for high geometric consistency, defined by low reprojection errors and a high inlier count, yielding a high-quality subset of 37,000 sequences (740,000 frames) selected from approximately 2 million processed frames.

Public Datasets

REAL-colon dataset: https://plus.figshare.com/articles/media/REAL-colon_dataset/22202866

Training Dataset:

Data Modality: Video

Video Training Data Size: Less than 10,000 Hours

Data Collection Method by dataset: Human

Properties: 29,535 sequences from colonoscopy recordings (REAL-colon dataset).

Testing Dataset:

Data Collection Method by dataset: Human

Labeling Method by dataset: Not Applicable

Properties 8,137 sequences from colonoscopy recordings (REAL-colon dataset), selected from distinct clinical procedures to better assess generalization.

Evaluation Dataset:

Benchmark Score

Camera pose:

ATE (dm): 0.012
RTE (cm): 0.003
ROT (deg): 0.043

Depth error:

L1 (cm): 0.161
Relative error (%): 4.14
RMSE (cm): 0.22

Data Collection Method by dataset: Human

Properties 8,137 sequences from colonoscopy recordings (REAL-colon dataset), selected from distinct clinical procedures to better assess generalization.

Inference:

Acceleration Engine: TensorRT

Test Hardware:

A100, RTX 6000 ADA, RTX A6000

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 Bias, Explainability, Safety & Security, and Privacy Subcards.

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.

License

Refer to the license agreement for use of the sample data.

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(2024). Fast encoder-based 3d from casual videos via point track processing. Advances in Neural Information Processing Systems, 37, 96150-96180.](https://tracks-to-4d.github.io/ )\n\n## Model\n\n### Description:\nA real-time, unsupervised method for generating dynamic 4D point clouds and accurate camera tracking from colonoscopy videos, with 27x better camera pose and 1.6mm depth accuracy. Since it is unsupervised, it eliminates the need to obtain ground-truth 3D data in clinical settings. This model is ready for commercial use.\n\nFor an application of this model in Holoscan SDK, please see [Holohub Link]().\n\n### License/Terms of Use:\nNVIDIA Open Model License\n\n### Deployment Geography:\nGlobal\n\n### Use Case:\nClinical colonoscopy procedures for real-time 3D reconstruction and camera tracking.\n\n## References(s):\nTracksTo4D: [Kasten, Y., Lu, W., & Maron, H. (2024). Fast encoder-based 3d from casual videos via point track processing. Advances in Neural Information Processing Systems, 37, 96150-96180.](https://tracks-to-4d.github.io/ )\n\n## Model Architecture:\n- **Architecture Type:** Transformer\n- **Network Architecture:** TracksTo4D\n- **Number of model parameters:** 12M\n\n## Input(s):\n- **Input Type(s):** 2D Point Tracking from Colonoscopy Videos\n- **Input Format(s):** Tensor\n- **Input Parameters:** Two-Dimensional (2D) \n- **Other Properties Related to Input:** The model takes 2D feature tracks from colonoscopy videos as input. Tracking points projected to the screen space.\n\n## Output(s)\n- **Output Type(s):** 4D Point Cloud, Camera Pose\n- **Output Format(s):** Tensor, Tensor\n- **Output Parameters:** Three-Dimensional (3D), Three-Dimensional (3D)\n- **Other Properties Related to Output:** Dynamic 4D point clouds and 6-DoF camera tracking.\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- TensorRT\n\n**Supported Hardware Microarchitecture Compatibility:**\n- NVIDIA Ampere\n- NVIDIA Blackwell\n- NVIDIA Hopper\n\n**Preferred/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\nThis AI model can be used with Holoscan for optimal performance.\n\n## Model Version(s):\nv1.0\n\n## Training, Testing, and Evaluation Datasets:\n\n### Dataset Overview\nTotal Size: 2.7 Million Frames\nTotal Number of Datasets: 1\nDataset partition: 100%\n\nGiven that the raw footage suffered from quality issues such as motion blur, fluid occlusion, and camera-wall collisions, we implemented a geometric curation pipeline based on Structure-from-Motion (SfM). We processed the data into 20-frame sequences, estimating camera poses and sparse 3D structure, followed by refinement via Bundle Adjustment and metric auto-calibration. Crucially, this pipeline handles the dynamic nature of the colon by isolating a subset of static points that satisfy rigid multiview constraints. Sequences were then automatically filtered for high geometric consistency, defined by low reprojection errors and a high inlier count, yielding a high-quality subset of 37,000 sequences (740,000 frames) selected from approximately 2 million processed frames. \n\n# Public Datasets\nREAL-colon dataset: https://plus.figshare.com/articles/media/REAL-colon_dataset/22202866\n\n## Training Dataset:\nData Modality: Video\n\nVideo Training Data Size: Less than 10,000 Hours\n\nData Collection Method by dataset: Human\n\n**Properties:** 29,535 sequences from colonoscopy recordings (REAL-colon dataset).\n\n### Testing Dataset:\nData Collection Method by dataset: Human\n\nLabeling Method by dataset: Not Applicable\n\n**Properties** 8,137 sequences from colonoscopy recordings (REAL-colon dataset), selected from distinct clinical procedures to better assess generalization.\n\n### Evaluation Dataset:\n#### Benchmark Score\nCamera pose:\n- ATE (dm): 0.012\n- RTE (cm): 0.003\n- ROT (deg): 0.043\n\nDepth error:\n- L1 (cm): 0.161\n- Relative error (%): 4.14\n- RMSE (cm): 0.22\n\nData Collection Method by dataset: Human\n\n**Properties** 8,137 sequences from colonoscopy recordings (REAL-colon dataset), selected from distinct clinical procedures to better assess generalization.\n\n# Inference:\n**Acceleration Engine:** TensorRT\n\n**Test Hardware:**\n* A100, RTX 6000 ADA, RTX A6000\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\nFor more detailed information on ethical considerations for this model, please see the Bias, Explainability, Safety & Security, and Privacy Subcards.\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://app.intigriti.com/programs/nvidia/nvidiavdp/detail).\n\n## License\nRefer to the [license agreement](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf) for use of the sample data.","displayName","Holoscan Tracking Sample App Data",["D",1773339176602],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/clara-holoscan/resources/holoscan_tracking_sample_data","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[270,271,272,273,274,275],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_240":296,"_277":296,"_278":297,"_280":87},{"_240":292,"_277":293,"_278":294,"_280":87},{"_240":288,"_277":289,"_278":290,"_280":87},{"_240":285,"_277":285,"_278":286,"_280":87},{"_240":283,"_277":283,"_278":284,"_280":87},{"_240":276,"_277":276,"_278":279,"_280":87},"Torch-TRT","value","filter",{"_281":282,"_277":276},"category","field","framework","NSPECT-MKYV-61MH",{"_281":87,"_277":283},"Model",{"_281":287,"_277":285},"type","Holoscan","pltfm_clara_holoscan",{"_281":291,"_277":289},"platform","Healthcare","indus_healthcare",{"_281":295,"_277":293},"industry","Dataset",{"_281":287,"_277":296},{"_152":1472,"_154":1473},"ngcApps",[1425],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1406":1407,"_1408":1409,"_1410":1411,"_1412":1413,"_1414":309,"_109":333,"_1415":1416,"_1417":59,"_1418":90,"_1419":90,"_1420":1421,"_1422":90,"_1423":1424},"amplitude",{"_1403":1404,"_1006":-7,"_1405":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1397":1398,"_1399":1400,"_1401":1402},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1281":128,"_1282":1283,"_1284":1285,"_1286":1287},"announcements",[],"consentState",{"_371":1271,"_1272":90,"_1273":34,"_1274":34,"_1275":1276},"consentConfig",{"_353":354,"_355":356,"_357":358,"_359":360,"_361":362,"_363":364,"_365":366,"_367":368,"_369":370,"_371":372},"showContactAdmin",1784017731201,[],[],[347,348,349,350,351,352],{"_48":62,"_50":73,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":69,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":67,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":63,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":147,"_54":-5,"_56":-5,"_58":59},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. It is either correlated to the 6suuid or a unique value, and can be changed during the lifetime of this cookie.This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","s_ips","This cookie is associated with Adobe Analytics. The cookie is used for measuring a visitor’s scroll activity to see how much of a page they view before moving on to another page.","_gd_session","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. 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The service also uses cookies for various purposes, including analytics, personalization, and advertising.","readme.io",[976],{"_112":951,"_418":974,"_419":34,"_211":977},"The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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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. It is a temporary cookie used to determine if the browser accepts cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","login_session","bm_ss","This cookie is used to help protect websites from automated bot traffic.","AWS ELB application load balancerUsed 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.","_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. Usually used to maintain an anonymised user session by the server.","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","ApplicationGatewayAffinityCORS","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure all requests from a user during the session are sent to the same server.","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.Supports Amplitude product analytics.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","AMP_TLDTEST","c_code","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. 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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