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.

","resourceVersions",[258,259],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_232":233,"_90":234,"_44":235,"_236":-5,"_237":238,"_93":239,"_240":241,"_242":16,"_243":26,"_244":-5,"_245":26,"_246":-5,"_247":26,"_248":-5,"_249":26,"_250":-5,"_251":26,"_252":-5,"_253":26,"_254":-5,"_255":26,"_256":257},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/tao/models/transfusion/-","version","-","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/tao/transfusion:v1.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/tao/models/transfusion/-/version-history","accessCheckResult",{"_231":155},"hasRelatedCollections","context",{"_84":85,"_86":87},1784032783834,[],[],[224,225,226,227,228,229,230],{"_40":55,"_42":65,"_44":57,"_46":58,"_48":66,"_50":51},{"_40":55,"_42":63,"_44":57,"_46":58,"_48":64,"_50":51},{"_40":55,"_42":61,"_44":57,"_46":58,"_48":62,"_50":51},{"_40":55,"_42":60,"_44":57,"_46":58,"_48":59,"_50":51},{"_40":55,"_42":56,"_44":171,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":54,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51},"allowed","versionId","v1.0","","UPLOAD_COMPLETE","releaseType","createdDate",["D",1702063600562],["D",1740843013362],"totalSizeInBytes",487092553,"totalFileCount","isSigned","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_77":208,"_260":262,"_236":-5,"_243":26,"_245":26,"_256":261,"_247":26},{"_77":233,"_260":233,"_236":-5,"_243":26,"_245":26,"_256":261,"_247":26},"name",[],"Latest (v1.0)","nvidia/tao/transfusion","nvidia/tao/models/transfusion","transfusion","Transfusion model to detect 3D objects from pointcloud and RGB data.","# 3D Fusion Model Card\n\n## Model Overview \n\nThe 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. \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.","Transfusion for 3D Object Detection",["D",1732745375782],"/api/image/nvidia/tao/models/transfusion","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png",445702016,"bias","explainability","privacy","safetyAndSecurity",[],[282,283,284,285,286,287,288,289],[],[],[],{"_91":144,"_128":145,"_129":304,"_131":132},{"_91":302,"_128":302,"_129":303,"_131":132},{"_91":300,"_128":300,"_129":301,"_131":132},{"_91":298,"_128":298,"_129":299,"_131":132},{"_91":296,"_128":296,"_129":297,"_131":132},{"_91":294,"_128":294,"_129":295,"_131":132},{"_91":92,"_128":92,"_129":293,"_131":132},{"_91":290,"_128":290,"_129":291,"_131":132},"TAO Toolkit",{"_133":292,"_128":290},"framework",{"_133":132,"_128":92},"Optical Inspection",{"_133":132,"_128":294},"NSPECT-K61X-1669",{"_133":132,"_128":296},"Industrial Inspection",{"_133":132,"_128":298},"Deep Learning",{"_133":132,"_128":300},"CV",{"_133":132,"_128":302},{"_133":137,"_128":145},{"_176":1473,"_178":1474},"ngcApps",[1426],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1407":1408,"_1409":1410,"_1411":1412,"_1413":1414,"_1415":316,"_207":340,"_1416":1417,"_1418":51,"_1419":155,"_1420":155,"_1421":1422,"_1423":155,"_1424":1425},"amplitude",{"_1404":1405,"_1011":-7,"_1406":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1398":1399,"_1400":1401,"_1402":1403},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1286":234,"_1287":1288,"_1289":1290,"_1291":1292},"announcements",[],"consentState",{"_377":1276,"_1277":155,"_1278":26,"_1279":26,"_1280":1281},"consentConfig",{"_359":360,"_361":362,"_363":364,"_365":366,"_367":368,"_369":370,"_371":372,"_373":374,"_375":376,"_377":378},"showContactAdmin",1784032783724,[],[],[354,355,356,357,358],{"_40":55,"_42":65,"_44":171,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":63,"_44":171,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":171,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":171,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":54,"_50":51},"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. 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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.","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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