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Field	Response
Participation considerations from adversely impacted groups (protected classes) in model design and testing:	No measures taken to mitigate against unwanted bias.
Measures taken to mitigate against unwanted bias:	None of the Above.

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TAO BEVFusion Model Card

Model Overview

Description

BEVFusion model detects the people within an image and a Lidar with 3D Bounding Boxes, a category label for each object and confidence scores.

This model is ready for commercial use.

License/Terms of Use

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

References

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)

Model Architecture

Architecture Type: Vision Transformer
\nNetwork Architecture:\nThis model is a transformer-based network archteicture and fuses the lidar and camear features in Bird's-Eye View Feature space to perform 3D object detection.\nSwin-Tiny (Image Backbone); SECOND (LiDAR Backbone) were used.

This model was trained using the BEVFusion entrypoint in TAO. The model uses three angles in 3D space (roll, pitch ,yaw) to represent the 3D boudning boxes, whereas only single angle (yaw) was supported in the original BEVFusion code base.\nThe training algorithm optimizes the network to minimize the Gaussian Focal Loss and L1 Loss.

Input

Input Types:

Image , Point Cloud

Input Formats:

Images: Red, Green, Blue (RGB)
Point Cloud: Points

Input Parameters:

Images - 1920 X 1080 X 3 for TAO3DSynthetic: Two Dimensional (2D)
Images - 370 X 1224 X for KittiPerson : Two Dimensional (2D)
Point Cloud - N X 4 : Three Dimensional (3D)

Other Properties Related to Input:

Calibration matrix: Lidar to Camera Prjoection Matrix, Camera Intrinsic Matrix
No specific minimum or maximum resolution restriction, No Alpha Channel needed

Output

Output Types: 3D Bounding Boxes, Category labels, Confidence scores
\nOutput Format: Images with 3D Bounding Boxes visualization
\nOutput Parameters: Nine Dimensional (9D), One-Dimensional (1D), One-Dimensional (1D)
\nOther Properties Related to Output:

3D bounding-box coordinates: (Cetner_x, Cetner_y, Center_z, Scale_x, Scale_y, Scale_z, Rotation_x, Rotation_y, Rotation_z).
No specific minimum or maximum resolution restriction, No Alpha Channel needed

Software Integration

Runtime Engines:

TAO 5.5.0

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Blackwell
NVIDIA Jetson
NVIDIA Hopper
NVIDIA Lovelace
NVIDIA Pascal
NVIDIA Turing
NVIDIA Volta

[Preferred/Supported] Operating Systems:

Linux

These models are designed for use with NVIDIA hardware and software. For hardware, the models are compatible with any NVIDIA GPU, including NVIDIA Jetson devices. For software, the models are specifically designed for the TAO Toolkit.

The primary application of these models is to estimate an object's 3D bounding box from a single camera and sinlge lidar sensor setup. They can identify the person objects given single rgb image with aligned single lidar point cloud.

Model Versions

bevfusion1.0**: pytorch pth file, inferencable on TAO Toolkit.

Training, Testing, and Evaluation Datasets

Training Dataset

A proprietary synthetic dataset (TAO3DSynthetic) that was generated with Omniverse and Issac-Sim was used during the training and testing. The training dataset has 465,373 images, point cloud pairs, and 1,510,704 objects for the person class.

nSpect: NSPECT-I6RW-ENUU

Data Collection Method by Dataset

Synthetic

Labeling Method by Dataset

Synthetic

Properties:
\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

Evaluation Dataset

Evaluation dataset for TAO3DSynthetic was also generated along with training dataset.\nEvaluation dataset contains three scenes (Warehouse, Retail Store and Hospital) from training data with unseen people in them and one new scene that is not included in the training dataset.

Data Collection Method by Dataset

Synthetic

Labeling Method by Dataset

Synthetic

After pretraining is complete on TAO3DSynthetic, the BEVFusion model was fine-tuned on a public KITTI dataset with only pedestrian class to demonstrate the use-case. We do not release the model weights for Kitti fine-tuning and we are showing accuracy results only below for demonstration purpose.\nThe original Kitti provides only rotation_y in 3D bounding box labels. Thus, we zero-padded rotation x and z for KittiPerson dataset to be compatible with TAO3DSynthetic-BEVfusion.\nWe only used the images that contains pedestrian from original Kitti dataset. KittiPerson dataset consists of 955 training images with 2207 person objects and 824 validation images with 2280 person objects.

Methodology and KPI

The KPI for the evaluation data are reported in the following table.\nFor TAO3Dsynthetic-BEVFusion model, the evaluation data is also generated with Omniverse.\nFor KittiPerson-BEVFusion model, validation set from Kitti was used for evaluation. Both training from scrtach and finetuning from TAO3DSynthetic were evaluated for KittiPerson model. Two models were evaluated with the KITTI 3D Metric, which evaluates object detection performance using AP40 at IOU 0.5.\nNote that Kitti Pedestrian is more chanllenging data as it was captured in the real street scenes.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Model	TAO3DSynthetic-BEVFusion
From Scratch	79.35%

\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	KittiPerson-BEVFusion
From Scratch	38.383 %
From TAO3DSynthetic-Finetuned	53.8747 %

Inference:

Engine: Pytorch

Inference Method

BEVFusion inference will be run through tao_pytorch_backend.

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 Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards [Insert Link to Model Card++ here]. Please report security vulnerabilities or NVIDIA AI Concerns here.

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\n**Network Architecture:** \nThis model is a transformer-based network archteicture and fuses the lidar and camear features in Bird's-Eye View Feature space to perform 3D object detection. \nSwin-Tiny (Image Backbone); SECOND (LiDAR Backbone) were used.\n\nThis model was trained using the BEVFusion entrypoint in TAO. The model uses three angles in 3D space (roll, pitch ,yaw) to represent the 3D boudning boxes, whereas only single angle (yaw) was supported in the original BEVFusion code base.\nThe training algorithm optimizes the network to minimize the Gaussian Focal Loss and L1 Loss.\n\n### Input\n**Input Types:**
\n* Image , Point Cloud
\n\n**Input Formats:**
\n* Images: Red, Green, Blue (RGB)
\n* Point Cloud: Points
\n\n**Input Parameters:**
\n* Images - 1920 X 1080 X 3 for TAO3DSynthetic: Two Dimensional (2D)\n* Images - 370 X 1224 X for KittiPerson : Two Dimensional (2D)\n* Point Cloud - N X 4 : Three Dimensional (3D)\n\n**Other Properties Related to Input:** \n* Calibration matrix: Lidar to Camera Prjoection Matrix, Camera Intrinsic Matrix
\n* No specific minimum or maximum resolution restriction, No Alpha Channel needed
\n\n### Output\n**Output Types:** 3D Bounding Boxes, Category labels, Confidence scores
\n**Output Format:** Images with 3D Bounding Boxes visualization
\n**Output Parameters:** Nine Dimensional (9D), One-Dimensional (1D), One-Dimensional (1D)
\n**Other Properties Related to Output:**
\n* 3D bounding-box coordinates: (Cetner_x, Cetner_y, Center_z, Scale_x, Scale_y, Scale_z, Rotation_x, Rotation_y, Rotation_z).
\n* No specific minimum or maximum resolution restriction, No Alpha Channel needed
\n\n## Software Integration \n**Runtime Engines:** \n* TAO 5.5.0
\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Ampere
\n* NVIDIA Blackwell
\n* NVIDIA Jetson
\n* NVIDIA Hopper
\n* NVIDIA Lovelace
\n* NVIDIA Pascal
\n* NVIDIA Turing
\n* NVIDIA Volta
\n\n**[Preferred/Supported] Operating Systems:**
\n* Linux
\n\nThese models are designed for use with NVIDIA hardware and software. For hardware, the models are compatible with any NVIDIA GPU, including NVIDIA Jetson devices. For software, the models are specifically designed for the [TAO Toolkit](https://github.com/NVIDIA/tao_pytorch_backend).\n\nThe primary application of these models is to estimate an object's 3D bounding box from a single camera and sinlge lidar sensor setup. They can identify the person objects given single rgb image with aligned single lidar point cloud.\n\n## Model Versions\n\nbevfusion1.0**: pytorch pth file, inferencable on [TAO Toolkit](https://github.com/NVIDIA/tao_pytorch_backend).\n\n# Training, Testing, and Evaluation Datasets \n\n## Training Dataset \nA proprietary synthetic dataset (TAO3DSynthetic) 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 and testing. The training dataset has 465,373 images, point cloud pairs, and 1,510,704 objects for the person class. \n* nSpect: NSPECT-I6RW-ENUU\n\n**Data Collection Method by Dataset**
\n* Synthetic
\n\n**Labeling Method by Dataset**
\n* Synthetic
\n\n**Properties:**
\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## Evaluation Dataset \n\nEvaluation dataset for TAO3DSynthetic was also generated along with training dataset.\nEvaluation dataset contains three scenes (Warehouse, Retail Store and Hospital) from training data with unseen people in them and one new scene that is not included in the training dataset.\n\n**Data Collection Method by Dataset**
\n* Synthetic
\n\n**Labeling Method by Dataset**
\n* Synthetic
\n\nAfter pretraining is complete on TAO3DSynthetic, the BEVFusion model was fine-tuned on a public [KITTI dataset](https://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d) with only pedestrian class to demonstrate the use-case. We do not release the model weights for Kitti fine-tuning and we are showing accuracy results only below for demonstration purpose.\nThe original Kitti provides only rotation_y in 3D bounding box labels. Thus, we zero-padded rotation x and z for KittiPerson dataset to be compatible with TAO3DSynthetic-BEVfusion. \nWe only used the images that contains pedestrian from original Kitti dataset. KittiPerson dataset consists of 955 training images with 2207 person objects and 824 validation images with 2280 person objects.\n\n### Methodology and KPI\n\nThe KPI for the evaluation data are reported in the following table. \nFor TAO3Dsynthetic-BEVFusion model, the evaluation data is also generated with Omniverse. \nFor KittiPerson-BEVFusion model, validation set from Kitti was used for evaluation. Both training from scrtach and finetuning from TAO3DSynthetic were evaluated for KittiPerson model. Two models were evaluated with the KITTI 3D Metric, which evaluates object detection performance using AP40 at IOU 0.5. \nNote that Kitti Pedestrian is more chanllenging data as it was captured in the real street scenes.\n\n| Model| TAO3DSynthetic-BEVFusion | \n|------|------|\n|From Scratch| 79.35% |\n\n| Model| KittiPerson-BEVFusion | \n|------|------|\n|From Scratch| 38.383 % |\n|From TAO3DSynthetic-Finetuned| 53.8747 % |\n\n## Inference:\n**Engine:** Pytorch
\n\n## Inference Method\n\nBEVFusion inference will be run through [tao_pytorch_backend](https://github.com/NVIDIA/tao_pytorch_backend/tree/main/).\n\n### Ethical Considerations \n\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. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards [Insert Link to Model Card++ here]. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","BEVFusion for 3D Object Detection",["D",1732745394928],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/bevfusion","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","| Field | Response |\n| -- | -- |\n|Participation considerations from adversely impacted groups [(protected classes)](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | No measures taken to mitigate against unwanted bias. |\n| Measures taken to mitigate against unwanted bias: | None of the Above. |","explainability","| Field | Response |\n| -- | -- |\n| Intended Application(s) & Domain(s): | Object Detection (People). |\n| Model Type: | 3D Object Detection. |\n| Intended Users: | This model is intended for developers working in industrial, robotics, and smart space applications. |\n| Output: | 3-D Bounding Box(es), Category Label(s), Confidence Score(s) |\n| Describe how the model works: | Detects one or more objects from fused LIDAR point cloud features and camera RGB features. |\n| Technical Limitations: | This model may not work well for detecting partially or fully occluded persons. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Mean Average Precision (mAP) |\n| Licensing: | [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) |","privacy","| Field | Response |\n| -- | -- |\n| Generatable or reverse engineerable personally-identifiable information ? | None |\n| Protected classes used to create this model? | Not Applicable (No personal data) |\n| Was consent obtained for any personal data used? | Not Applicable (No personal data) |\n| How often is dataset reviewed? | \tBefore Release |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable |\n| If personal data collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable |\n| If personal data collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects?\t| Not Applicable |\n| If personal data collected for the development of this AI model, was it minimized to only what was required? | Not Applicable |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? 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