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.","Holoscan Tracking Sample App Data",["D",1773339176602],"/api/image/nvidia/clara-holoscan/resources/holoscan_tracking_sample_data","bias","explainability","privacy","safetyAndSecurity",[],[270,271,272,273,274,275],[],[],[],{"_92":291,"_127":291,"_128":292,"_130":131},{"_92":287,"_127":288,"_128":289,"_130":131},{"_92":283,"_127":284,"_128":285,"_130":131},{"_92":280,"_127":280,"_128":281,"_130":131},{"_92":135,"_127":135,"_128":279,"_130":131},{"_92":276,"_127":276,"_128":277,"_130":131},"Torch-TRT",{"_132":278,"_127":276},"framework",{"_132":131,"_127":135},"Model",{"_132":282,"_127":280},"type","Holoscan","pltfm_clara_holoscan",{"_132":286,"_127":284},"platform","Healthcare","indus_healthcare",{"_132":290,"_127":288},"industry","Dataset",{"_132":282,"_127":291},{"_165":1463,"_167":1464},"ngcApps",[1416],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1397":1398,"_1399":1400,"_1401":1402,"_1403":1404,"_1405":304,"_196":328,"_1406":1407,"_1408":51,"_1409":144,"_1410":144,"_1411":1412,"_1413":144,"_1414":1415},"amplitude",{"_1394":1395,"_1000":-7,"_1396":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1388":1389,"_1390":1391,"_1392":1393},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1275":238,"_1276":1277,"_1278":1279,"_1280":1281},"announcements",[],"consentState",{"_366":1265,"_1266":144,"_1267":26,"_1268":26,"_1269":1270},"consentConfig",{"_348":349,"_350":351,"_352":353,"_354":355,"_356":357,"_358":359,"_360":361,"_362":363,"_364":365,"_366":367},"showContactAdmin",1783987115159,[],[],[342,343,344,345,346],{"_40":55,"_42":66,"_44":160,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":160,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":160,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":347,"_50":51},{"_40":41,"_42":43,"_44":160,"_46":-5,"_48":-5,"_50":51},3,"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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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. 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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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