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3D Fusion Model Card

Model Overview

The model described in this card detects the people within an image and a Lidar with 3D bounding boxes. It also provides a category label for each object.

Model Architecture

This model is based on BEVFusion, which unifies the feature representation from different modalities (Lidar and image). This unified feature can be used for 3D Object Detection tasks.

Training

The BEVFusion codebase from mmdet3d was used to train this model. The code was modified to accomodate three angles in 3D space (roll, pitch ,yaw), whereas only yaw was supported in the original code base.\nThe training algorithm optimizes the network to minimize the Gaussian Focal Loss and L1 Loss.

Training Data

A proprietary synthetic dataset that was generated with Omniverse and Issac-Sim was used during the training. The training dataset has 465,373 images, point cloud pairs, and 1,510,704 objects for the person class.\nThe training dataset consists of image, point cloud data from Lidar, and calibration matrices from nine different indoor scenes.\nThe content was captured from a height of two feet with sensors that were located perpendicularly from the surface. Each scene has three different lighting conditions: Normal, Dark, and Very Dark.

\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\">

	Training Dataset
Scene Category	Number of Images
Warehouse	41526
Retail Store	79283
Hospital	57182
Office	145940
Factory	141442
Total	465373

Performance

Methodology and KPI

The KPI for the evaluation data are reported in the following table. Evaluation data is also generated with Omniverse.\nThe model was evaluated with the KITTI 3D Metric, which evaluates object detection performance using mean Average Precision (mAP) at IOU 0.5.

\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\">

Model		BEVFusion
Content	AP11	AP40
Evaluation set	77.71%	79.35%

Input

RGB Image : 1920 X 1080 X 3\nLiDAR Point Cloud : N x 4 (N: nubmer of points, 4: xyz+intensity)\nCalibration matrix : Lidar to Camera prjoection matrix, Camera Intrinsic matrix

Output

Category labels (people) and 3D bounding-box coordinates in nine dimension representation (cetner_x,cetner_y,center_z, scale_x, scale_y, scale_z, rotation_x, rotation_y, rotation_z) for each detected people in the input image.

References

Citations

Liu, Zhijian and Tang, Haotian and Amini, Alexander and Yang, Xingyu and Mao, Huizi and Rus, Daniela and Han, Song: BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation. In: ICRA. (2023)

Technical Blogs

Access the latest in Vision AI development workflows with NVIDIA TAO Toolkit 5.0
Improve accuracy and robustness of vision AI models with vision transformers and NVIDIA TAO
Developing and Deploying AI-powered Robots with NVIDIA Isaac Sim and NVIDIA TAO
Learn endless ways to adapt and supercharge your AI workflows with TAO - Whitepaper

License

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

Ethical Considerations

NVIDIA 3D Fusion model detects people .

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.

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It also provides a category label for each object. \n\n## Model Architecture \n\nThis model is based on BEVFusion, which unifies the feature representation from different modalities (Lidar and image). This unified feature can be used for 3D Object Detection tasks. \n\n## Training \n\nThe [BEVFusion](https://arxiv.org/pdf/2205.13542.pdf) codebase from mmdet3d was used to train this model. The code was modified to accomodate three angles in 3D space (roll, pitch ,yaw), whereas only yaw was supported in the original code base. \nThe training algorithm optimizes the network to minimize the Gaussian Focal Loss and L1 Loss.\n\n### Training Data \nA proprietary synthetic dataset that was generated with [Omniverse](https://www.nvidia.com/en-us/omniverse/) and [Issac-Sim](https://developer.nvidia.com/isaac-sim) was used during the training. The training dataset has 465,373 images, point cloud pairs, and 1,510,704 objects for the person class. \nThe training dataset consists of image, point cloud data from Lidar, and calibration matrices from nine different indoor scenes. \nThe content was captured from a height of two feet with sensors that were located perpendicularly from the surface. Each scene has three different lighting conditions: Normal, Dark, and Very Dark. \n\n| | Training Dataset |\n| -- | -- |\n| Scene Category | Number of Images |\n| Warehouse | 41526 |\n| Retail Store | 79283 |\n| Hospital | 57182 |\n| Office | 145940 | \n| Factory | 141442 | \n| Total | 465373 | \n\n## Performance\n\n#### Methodology and KPI\n\nThe KPI for the evaluation data are reported in the following table. Evaluation data is also generated with Omniverse.\nThe model was evaluated with the KITTI 3D Metric, which evaluates object detection performance using mean Average Precision (mAP) at IOU 0.5. \n\n| Model | | BEVFusion | \n|---|---|---|\n| Content | AP11 | AP40 |\n|Evaluation set| 77.71% | 79.35% |\n\n### Input\n\nRGB Image : 1920 X 1080 X 3\nLiDAR Point Cloud : N x 4 (N: nubmer of points, 4: xyz+intensity)\nCalibration matrix : Lidar to Camera prjoection matrix, Camera Intrinsic matrix\n\n### Output\n\nCategory labels (people) and 3D bounding-box coordinates in nine dimension representation (cetner_x,cetner_y,center_z, scale_x, scale_y, scale_z, rotation_x, rotation_y, rotation_z) for each detected people in the input image.\n\n## References\n\n### Citations \n\n- Liu, Zhijian and Tang, Haotian and Amini, Alexander and Yang, Xingyu and Mao, Huizi and Rus, Daniela and Han, Song: BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation. In: ICRA. (2023)\n\n### Technical Blogs \n\n- [Access the latest in Vision AI development workflows with NVIDIA TAO Toolkit 5.0](https://developer.nvidia.com/blog/access-the-latest-in-vision-ai-model-development-workflows-with-nvidia-tao-toolkit-5-0/)\n- [Improve accuracy and robustness of vision AI models with vision transformers and NVIDIA TAO](https://developer.nvidia.com/blog/improve-accuracy-and-robustness-of-vision-ai-apps-with-vision-transformers-and-nvidia-tao/)\n- [Developing and Deploying AI-powered Robots with NVIDIA Isaac Sim and NVIDIA TAO](https://developer.nvidia.com/blog/developing-and-deploying-ai-powered-robots-with-nvidia-isaac-sim-and-nvidia-tao/)\n- Learn endless ways to adapt and supercharge your AI workflows with TAO - [Whitepaper](https://developer.nvidia.com/tao-toolkit-usecases-whitepaper/1-introduction)\n\n### Suggested Reading \n\n- More information on about TAO Toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/tao-toolkit)\n- [TAO documentation](https://docs.nvidia.com/tao/tao-toolkit/index.html)\n- Read the [TAO getting Started](https://docs.nvidia.com/tao/tao-toolkit/text/tao_quick_start_guide.html) guide and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html).\n- If you have any questions or feedback, see the discussions on [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17)\n- Learn more about [Issac-Sim]([Issac-Sim](https://developer.nvidia.com/isaac-sim))\n\n## License \n\nLicense to use these models is covered by the Model EULA. By downloading the model, you accept the terms and conditions of these [licenses](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/).\n\n## Ethical Considerations \n\nNVIDIA 3D Fusion model detects people . \n\nNVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.","displayName","Transfusion for 3D Object Detection",["D",1732745375782],"publisher","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/transfusion","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",445702016,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[237,238,239,240,241,242,243,244],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_205":265,"_246":266,"_247":267,"_249":250},{"_205":263,"_246":263,"_247":264,"_249":250},{"_205":261,"_246":261,"_247":262,"_249":250},{"_205":259,"_246":259,"_247":260,"_249":250},{"_205":257,"_246":257,"_247":258,"_249":250},{"_205":255,"_246":255,"_247":256,"_249":250},{"_205":253,"_246":253,"_247":254,"_249":250},{"_205":245,"_246":245,"_247":248,"_249":250},"TAO Toolkit","value","filter",{"_251":252,"_246":245},"category","label","field","framework","TAO",{"_251":250,"_246":253},"Optical Inspection",{"_251":250,"_246":255},"NSPECT-K61X-1669",{"_251":250,"_246":257},"Industrial Inspection",{"_251":250,"_246":259},"Deep Learning",{"_251":250,"_246":261},"CV",{"_251":250,"_246":263},"Computer Vision","soln_computer_vision",{"_251":268,"_246":266},"technology",{"_106":1443,"_108":1444},"ngcApps",[1396],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1377":1378,"_1379":1380,"_1381":1382,"_1383":1384,"_1385":280,"_137":304,"_1386":1387,"_1388":56,"_1389":85,"_1390":85,"_1391":1392,"_1393":85,"_1394":1395},"amplitude",{"_1374":1375,"_976":-7,"_1376":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1368":1369,"_1370":1371,"_1372":1373},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1251":167,"_1252":1253,"_1254":1255,"_1256":1257},"announcements",[],"consentState",{"_342":1241,"_1242":85,"_1243":31,"_1244":31,"_1245":1246},"consentConfig",{"_324":325,"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341,"_342":343},"showContactAdmin",1784116460018,[],[],[318,319,320,321,322],{"_45":59,"_47":69,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":67,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":323,"_55":56},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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It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[422],{"_77":423,"_389":420,"_390":31,"_166":424},"sp","Testing","google.com",[427],{"_77":428,"_389":425,"_390":31,"_166":429},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","tags.srv.stackadapt.com",[432,433,434,435],{"_77":440,"_389":430,"_390":31,"_166":437},{"_77":439,"_389":430,"_390":31,"_166":437},{"_77":438,"_389":430,"_390":31,"_166":437},{"_77":436,"_389":430,"_390":31,"_166":437},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[443],{"_77":444,"_389":441,"_390":31,"_166":445},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. For this reason it is classified as a primarily tracking/targeting domain.","twitter.com",[448,449,450,451],{"_77":457,"_389":446,"_390":31,"_166":453},{"_77":455,"_389":446,"_390":31,"_166":456},{"_77":454,"_389":446,"_390":31,"_166":453},{"_77":452,"_389":446,"_390":31,"_166":453},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[460,461,462,463],{"_77":469,"_389":458,"_390":31,"_166":467},{"_77":468,"_389":458,"_390":31,"_166":467},{"_77":466,"_389":458,"_390":31,"_166":467},{"_77":464,"_389":458,"_390":31,"_166":465},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[472],{"_77":473,"_389":470,"_390":31,"_166":474},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[477],{"_77":478,"_389":475,"_390":31,"_166":479},"receive-cookie-deprecation","This domain is owned by im-apps.net which provides information and resources about various instant messaging (IM) applications to help users make informed decisions based on their communication needs.","influ2.com",[482],{"_77":483,"_389":480,"_390":31,"_166":424},"R","6sc.co",[486],{"_77":487,"_389":484,"_390":31,"_166":488},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. 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.",[615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635],[580,581,582,583,584],{"_383":475,"_205":475,"_166":167,"_356":606},{"_383":441,"_205":441,"_166":167,"_356":601},{"_383":594,"_205":594,"_166":167,"_356":595},{"_383":590,"_205":590,"_166":167,"_356":591},{"_383":585,"_205":585,"_166":167,"_356":586},"nvcrm.my.site.com",[587],{"_77":588,"_389":585,"_390":31,"_166":589},"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",[592],{"_77":593,"_389":590,"_390":31,"_166":424},"edge","github.com",[596,597],{"_77":600,"_389":594,"_390":31,"_166":599},{"_77":598,"_389":594,"_390":31,"_166":599},"logged_in","This domain is owned by Github. 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The data is aggregated and analyzed; therefore, it remains anonymous upon use.\n","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","_biz_nA","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website 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