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",[259,260],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_234":209,"_91":235,"_44":236,"_237":-5,"_238":239,"_94":240,"_241":242,"_243":16,"_244":26,"_245":-5,"_246":26,"_247":-5,"_248":26,"_249":-5,"_250":26,"_251":-5,"_252":26,"_253":-5,"_254":26,"_255":-5,"_256":26,"_257":258},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/tao/models/transfusion/-","version","v1.0","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/tao/transfusion:v1.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/tao/models/transfusion/v1.0/version-history","accessCheckResult",{"_233":156},"hasRelatedCollections","context",{"_85":86,"_87":88},1787772421331,[],[],[225,226,227,228,229,230,231],{"_40":55,"_42":66,"_44":57,"_46":58,"_48":67,"_50":51},{"_40":55,"_42":64,"_44":57,"_46":58,"_48":65,"_50":51},{"_40":55,"_42":62,"_44":57,"_46":58,"_48":63,"_50":51},{"_40":55,"_42":60,"_44":57,"_46":58,"_48":232,"_50":51},{"_40":55,"_42":56,"_44":57,"_46":58,"_48":59,"_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},79,"allowed","versionId","","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",[],{"_78":263,"_261":264,"_237":-5,"_244":26,"_246":26,"_257":262,"_248":26},{"_78":209,"_261":209,"_237":-5,"_244":26,"_246":26,"_257":262,"_248":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",[],[284,285,286,287,288,289,290,291],[],[],[],{"_92":145,"_129":146,"_130":306,"_132":133},{"_92":304,"_129":304,"_130":305,"_132":133},{"_92":302,"_129":302,"_130":303,"_132":133},{"_92":300,"_129":300,"_130":301,"_132":133},{"_92":298,"_129":298,"_130":299,"_132":133},{"_92":296,"_129":296,"_130":297,"_132":133},{"_92":93,"_129":93,"_130":295,"_132":133},{"_92":292,"_129":292,"_130":293,"_132":133},"TAO Toolkit",{"_134":294,"_129":292},"framework",{"_134":133,"_129":93},"Optical Inspection",{"_134":133,"_129":296},"NSPECT-K61X-1669",{"_134":133,"_129":298},"Industrial Inspection",{"_134":133,"_129":300},"Deep Learning",{"_134":133,"_129":302},"CV",{"_134":133,"_129":304},{"_134":138,"_129":146},{"_177":1280,"_179":1281},"ngcApps",[1233],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1214":1215,"_1216":1217,"_1218":1219,"_1220":1221,"_1222":318,"_208":344,"_1223":1224,"_1225":51,"_1226":156,"_1227":156,"_1228":1229,"_1230":156,"_1231":1232},"amplitude",{"_1211":1212,"_857":-7,"_1213":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1205":1206,"_1207":1208,"_1209":1210},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787772421253,"1.12.0","defaultSearchSuggestions",{"_1097":235,"_1098":1099,"_1100":1101,"_1102":1103},"announcements",[],"consentState",{"_384":1087,"_1088":156,"_1089":26,"_1090":26,"_1091":1092},"consentConfig",{"_366":367,"_368":369,"_370":371,"_372":373,"_374":375,"_376":377,"_378":379,"_380":381,"_382":383,"_384":385},"showContactAdmin",1787772421254,[],[],[358,359,360,361,362,363,364],{"_40":55,"_42":66,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":62,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":365,"_50":51},5,"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 helps track your activity on the platform, including how you interact with their embedded services on other websites.","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.",[614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638],[585,586,587,588,589],{"_424":499,"_92":499,"_91":235,"_398":609},{"_424":473,"_92":473,"_91":235,"_398":604},{"_424":599,"_92":599,"_91":235,"_398":600},{"_424":594,"_92":594,"_91":235,"_398":595},{"_424":590,"_92":590,"_91":235,"_398":591},"nvidia.partners",[592],{"_261":593,"_430":590,"_431":26,"_91":467},"edge","app.hushly.com",[596],{"_261":597,"_430":594,"_431":26,"_91":598},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[601],{"_261":602,"_430":599,"_431":26,"_91":603},"_octo","This domain is owned by Github. 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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 performance.",[697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725],[677,678,679,680],{"_424":692,"_92":692,"_91":235,"_398":693},{"_424":687,"_92":687,"_91":235,"_398":688},{"_424":486,"_92":486,"_91":235,"_398":684},{"_424":599,"_92":599,"_91":235,"_398":681},[682],{"_261":683,"_430":599,"_431":156,"_91":603},"_gh_sess",[685],{"_261":686,"_430":486,"_431":156,"_91":493},"YSC","bizible.com",[689],{"_261":690,"_430":687,"_431":26,"_91":691},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[694],{"_261":695,"_430":692,"_431":26,"_91":696},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_261":767,"_430":545,"_431":26,"_91":768},{"_261":752,"_430":766,"_431":156,"_91":753},{"_261":735,"_430":548,"_431":26,"_91":660},{"_261":735,"_430":648,"_431":26,"_91":660},{"_261":764,"_430":545,"_431":26,"_91":765},{"_261":752,"_430":551,"_431":156,"_91":753},{"_261":762,"_430":545,"_431":26,"_91":763},{"_261":752,"_430":548,"_431":156,"_91":753},{"_261":760,"_430":647,"_431":26,"_91":761},{"_261":752,"_430":669,"_431":156,"_91":753},{"_261":732,"_430":548,"_431":26,"_91":734},{"_261":758,"_430":545,"_431":156,"_91":759},{"_261":756,"_430":545,"_431":26,"_91":757},{"_261":754,"_430":545,"_431":156,"_91":755},{"_261":752,"_430":643,"_431":156,"_91":753},{"_261":750,"_430":545,"_431":26,"_91":751},{"_261":748,"_430":545,"_431":26,"_91":749},{"_261":746,"_430":545,"_431":156,"_91":747},{"_261":744,"_430":652,"_431":26,"_91":745},{"_261":732,"_430":643,"_431":26,"_91":734},{"_261":742,"_430":545,"_431":26,"_91":743},{"_261":740,"_430":545,"_431":156,"_91":741},{"_261":738,"_430":545,"_431":26,"_91":739},{"_261":736,"_430":545,"_431":26,"_91":737},{"_261":735,"_430":545,"_431":26,"_91":660},{"_261":732,"_430":733,"_431":26,"_91":734},{"_261":730,"_430":545,"_431":26,"_91":731},{"_261":728,"_430":545,"_431":156,"_91":729},{"_261":726,"_430":545,"_431":26,"_91":727},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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