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Field	Response
Participation considerations from adversely impacted groups (protected classes) in model design and testing:	None of the Above
Measures taken to mitigate against unwanted bias:	Not Applicable.

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FoundationPose Model Card

Model Overview

Description

FoundationPose is a unified foundation model for 6-DoF (Degrees of Freedom) object pose estimation and tracking. This approach can be instantly applied at test-time to a novel object without fine-tuning, as long as its CAD model is given. This model is ready for commercial use.

License

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

Bowen Wen, Wei Yang, Jan Kautz, Stan Birchfield. \"FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects.\" IEEE Conference on Computer Vision and Pattern Recognition. (CVPR). 2024.

Model Architecture

Architecture Type: Transformer-based Network Architecture

Network Architecture

Network Encoder: CNN (Convolutional Neural Network) blocks with residual connection
Network Decoder: Multi-head self-attention module

More Details

\n
This model contains a novel design of transformer-based network architectures and contrastive learning formulation, which leads to strong generalization ability. The FoundationPose model contains two seperate networks, refine net and score net.
\n
\n
The refinement network extracts feature maps from the two RGBD input branches with a single shared CNN encoder. The feature maps are concatenated, fed into CNN blocks with residual connection, and tokenized by dividing into patches with position embedding. Finally, the network predicts the translation update and rotation update, each individually processed by a transformer encoder and linearly projected to the output dimension.
\n
\n
The score net performs with a pose ranking encoder, utilizing the same backbone architecture for feature extraction as in the refinement network. The extracted features are concatenated, tokenized and forwarded to the multi-head self-attention module so as to leverage the global image context for comparison.
\n

Input

Input Types

RGB image
Depth image
2D bounding box
CAD model
Intrinsic matrix

Input Formats

RGB image: Red, Green, Blue (RGB). Can support any input resolution and images do not need any additional pre-processing (e.g. alpha channels or bits)
Depth image: Depth values. Can support any resolution and images do not need any additional pre-processing (e.g. alpha channels or bits)
CAD model: The CAD model is in OBJ format and includes the texture PNG image under the same folder
Intrinsic matrix: The input requires the correct camera calibration information, which includes principle points and focal length. The format is txt file
2D bounding box: The coordinate of the target object in the first frame, and the coordinates in xyxy format

Input Parameters: Multiple dimensions. See below for detailed input shapes\nOther Properties Related to Input:\n- RGB image: B X 3 X H X W (Batch Size x Channel x Height x Width)\n- Depth image: B X H X W (Batch Size x Height x Width)\n- 2D bounding box: B X 1 X 4 (Batch size X 1 X Bounding box coordinate)\n- CAD model: OBJ file\n- Intrinsic matrix: txt file

Output

Output Types: Testing image annotated with 3D bounding box, as well as the 6-DoF (Degrees of Freedom) axis\nOutput Format: Two Dimensional (2D) vectors\nOther Properties Related to Output:

pred_trans: 252 x 3 (Number of Hypothesis Poses x Translation)
pred_rot: 252 x 3 (Number of Hypothesis Poses x Rotation)
pred_score: 252 x 1 (Number of Hypothesis Poses x Pose Score)

Software Integration

Runtime Engines

TAO Triton Apps

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

Model Versions

Deployable_v1.0: decrypted ONNX files, inferencable on TAO Triton apps.

Training, Testing, and Evaluation Datasets

This model uses two networks to make pose estimations. The training of refine network is supervised by L2 loss, unifying both updates and input observation in the camera coordinate frame. To train the pose ranking network, we used a pose-conditioned triplet loss for optimizing the positive samples that close enough to the ground truth.

Training Dataset

Link

Synthetic Dataset: Generated by NVIDIA Omniverse.

Data Collection Method by Dataset

Purely Synthetic Data

Labeling Method by Dataset

Purely Synthetic Data

Properties

\n
The FoundationPosePose model was trained on two recent large scale 3D databases including Objaverse and GSO (Google Scanned Objects).For Objaverse, we chose the objects from the ObjaverseLVIS subset that consists of more than 40K objects belonging to 1,156 LVIS categories. This list contains the most relevant daily-life objects with reasonable quality, and diversity of shapes and appearances.
\n
\n
The synthetic data generation is implemented in NVIDIA Isaac Sim, leveraging path tracing for high-fidelity photo-realistic rendering. We perform gravity and physics simulation to produce physically plausible scenes. In each scene, we randomly sample objects with the original texture. In addition, the object size, material, camera pose, and lighting are also randomized.
\n

Evaluation Dataset

Link

LINEMOD: Provides additional ground-truth annotations for all modeled objects in one of the test sets from LM. This introduces challenging test cases with various levels of occlusion
YCB-V (YCB-Video): 21 YCB objects captured in 92 videos.

Data Collection Method by Dataset

Human

Labeling Method by Dataset

Human

Dataset Licenses

LINEMOD: CC BY 4.0
YCB-V (YCB-Video): MIT

Evaluation Results

Accuracy was determined using the following metrics:

Area under the curve (AUC) of ADD and ADD-S.
Recall of ADD that is less than 0.1 of the object diameter (ADD-0.1d).

In all evaluation, it always uses the same trained model and configurations for inference without any fine-tuning. The following table presents the results among RGBD methods on 3 core datasets from BOP. These involve various challenging scenarios (dense clutter, multi-instance, static or dynamic scenes, table-top or robotic manipulation), and objects with diverse properties (textureless, shiny, symmetric, varying sizes).

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

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Method	Unseen objects	Occluded-LINEMOD	TLESS	YCB-Video	Mean
FoundationPose	✓	78.8	83.0	88.0	83.3

Inference

Engine

Tensor(RT)

Test Hardware [Name the specific test hardware model]

A2
A30
DGX H100
DGX A100
DGX H100
JAO 64GB
Jetson AGX Xavier
L4
L40
NVIDIA T4
Orin
Orin Nano 8GB
Orin NX
Orin NX16GB
T4
Xavier NX

The inference performance of the provided FoundationPose model is evaluated at FP16 precisions. The model's input resolution is 6x160x160 pixels. The performance assessment was conducted using trtexec on a range of devices. In the table, \"BS\" stands for \"batch size.\"

The performance data presented pertains solely to model inference. The end-to-end performance, when integrated with streaming video data, the pre-processing and the post-processing, might different due to potential bottlenecks in hardware and software.

\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\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\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\">

Models (FP16)	Devices	Latency (BS=1)	Images per Second (BS=1)	Latency (BS=252)	Images per Second (BS=252)
FoundationPose - Refine Network	Orin Nano 8GB	4.12	242.59	1118.49	228.88
FoundationPose - Refine Network	Orin NX 16GB	2.85	350.39	768.46	333.13
FoundationPose - Refine Network	Orin AGX 64GB	1.10	908.91	300.26	852.59
FoundationPose - Refine Network	Tesla T4	5.62	182.74	1236.00	205.66
FoundationPose - Refine Network	A30	2.55	392.99	529.99	475.48
FoundationPose - Refine Network	A2	9.15	109.28	1985.32	126.93
FoundationPose - Refine Network	A100	1.57	638.29	266.14	949.55
FoundationPose - Refine Network	H100	1.16	878.56	123.09	2050.85
FoundationPose - Refine Network	L4	2.59	389.66	558.35	457.71
FoundationPose - Refine Network	L40	1.05	978.63	222.20	1145.97

\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\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\">

Models (FP16)	Devices	Latency (BS=252)	Images per Second (BS=252)
FoundationPose - Score Network	Orin NX 8GB	816.39	308.68
FoundationPose - Score Network	Orin NX 16GB	564.27	446.59
FoundationPose - Score Network	Orin AGX 64GB	210.02	1199.89
FoundationPose - Score Network	Tesla T4	1122.12	224.57
FoundationPose - Score Network	A30	394.69	638.48
FoundationPose - Score Network	A2	1702.51	148.55
FoundationPose - Score Network	A100	195.37	1289.84
FoundationPose - Score Network	H100	109.66	2301.73
FoundationPose - Score Network	L4	470.54	539.91
FoundationPose - Score Network	L40	196.02	1313.98

Output Image

\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\">

Input Image	Input CAD Model	Output Result

Limitations

Reflective Surface Objects

The FoundationPose might difficult to detect and track the object pose that appears reflective surface in different light conditions.

Inference Method

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 TensorRT.

The primary application of these models is to estimate an object's pose from a single RGBD image or a sequence of RGBD video. They can identify the objects in photos, given the right image pre-processing and post-processing procedures.

Furthermore, these models are designed for deployment to edge devices using the TensorRT. TAO Triton apps offers capabilities to construct efficient image analytic pipelines. These pipelines can capture, decode, and process data before executing inference.

Instructions to Deploy the Model with Triton Inference Server

To create the entire end-to-end inference application, deploy this model with Triton Inference Server. NVIDIA Triton Inference Server is an open-source inference serving software that helps standardize model deployment and execution and delivers fast and scalable AI in production. Triton supports direct integration of this model into the server and inference from a client.

To deploy this model with Triton Inference Server and end-to-end inference from images, please refer to the TAO Triton apps.

Ethical Considerations

NVIDIA FoundationPose model estimates the object pose. However, no additional information such as people and other distractors in the background are inferred.\nThe training and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies.\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.

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\n\n## License \n\nLicense 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](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\n\n## References\n\nBowen Wen, Wei Yang, Jan Kautz, Stan Birchfield. \"FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects.\" *IEEE Conference on Computer Vision and Pattern Recognition. (CVPR)*. 2024.\n\n## Model Architecture \n\n**Architecture Type:** Transformer-based Network Architecture\n\n**Network Architecture**\n- Network Encoder: CNN (Convolutional Neural Network) blocks with residual connection\n- Network Decoder: Multi-head self-attention module\n\n**More Details**\n- This model contains a novel design of transformer-based network architectures and contrastive learning formulation, which leads to strong generalization ability. The FoundationPose model contains two seperate networks, refine net and score net. \n\n- The refinement network extracts feature maps from the two RGBD input branches with a single shared CNN encoder. The feature maps are concatenated, fed into CNN blocks with residual connection, and tokenized by dividing into patches with position embedding. Finally, the network predicts the translation update and rotation update, each individually processed by a transformer encoder and linearly projected to the output dimension.\n\n- The score net performs with a pose ranking encoder, utilizing the same backbone architecture for feature extraction as in the refinement network. The extracted features are concatenated, tokenized and forwarded to the multi-head self-attention module so as to leverage the global image context for comparison. \n\n## Input\n\n**Input Types**\n- RGB image\n- Depth image\n- 2D bounding box\n- CAD model\n- Intrinsic matrix\n\n**Input Formats**\n- RGB image: Red, Green, Blue (RGB). Can support any input resolution and images do not need any additional pre-processing (e.g. alpha channels or bits)\n- Depth image: Depth values. Can support any resolution and images do not need any additional pre-processing (e.g. alpha channels or bits)\n- CAD model: The CAD model is in OBJ format and includes the texture PNG image under the same folder\n- Intrinsic matrix: The input requires the correct camera calibration information, which includes principle points and focal length. The format is txt file\n- 2D bounding box: The coordinate of the target object in the first frame, and the coordinates in `xyxy` format\n\n**Input Parameters:** Multiple dimensions. See below for detailed input shapes\n**Other Properties Related to Input:**\n - RGB image: B X 3 X H X W (Batch Size x Channel x Height x Width)\n - Depth image: B X H X W (Batch Size x Height x Width)\n - 2D bounding box: B X 1 X 4 (Batch size X 1 X Bounding box coordinate)\n - CAD model: OBJ file\n - Intrinsic matrix: txt file\n\n## Output\n\n**Output Types:** Testing image annotated with 3D bounding box, as well as the 6-DoF (Degrees of Freedom) axis\n**Output Format:** Two Dimensional (2D) vectors\n**Other Properties Related to Output:**\n - `pred_trans`: 252 x 3 (Number of Hypothesis Poses x Translation)\n - `pred_rot`: 252 x 3 (Number of Hypothesis Poses x Rotation)\n - `pred_score`: 252 x 1 (Number of Hypothesis Poses x Pose Score)\n\n## Software Integration \n\n**Runtime Engines**\n- TAO Triton Apps\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\n## Model Versions\n\n- **Deployable_v1.0**: decrypted [ONNX](https://onnx.ai/) files, inferencable on [TAO Triton apps](https://github.com/NVIDIA-AI-IOT/tao-toolkit-triton-apps).\n\n# Training, Testing, and Evaluation Datasets \n\nThis model uses two networks to make pose estimations. The training of refine network is supervised by L2 loss, unifying both updates and input observation in the camera coordinate frame. To train the pose ranking network, we used a pose-conditioned triplet loss for optimizing the positive samples that close enough to the ground truth.\n\n## Training Dataset \n\n**Link**\n- [Synthetic Dataset](https://github.com/NVlabs/FoundationPose?tab=readme-ov-file#training-data-download): Generated by NVIDIA Omniverse.\n\n**Data Collection Method by Dataset**\n- Purely Synthetic Data\n\n**Labeling Method by Dataset**\n- Purely Synthetic Data\n\n**Properties**\n- The FoundationPosePose model was trained on two recent large scale 3D databases including Objaverse and GSO (Google Scanned Objects).For Objaverse, we chose the objects from the ObjaverseLVIS subset that consists of more than 40K objects belonging to 1,156 LVIS categories. This list contains the most relevant daily-life objects with reasonable quality, and diversity of shapes and appearances.\n\n- The synthetic data generation is implemented in [NVIDIA Isaac Sim](https://developer.nvidia.com/isaac-sim), leveraging path tracing for high-fidelity photo-realistic rendering. We perform gravity and physics simulation to produce physically plausible scenes. In each scene, we randomly sample objects with the original texture. In addition, the object size, material, camera pose, and lighting are also randomized. \n\n

\n\n## Evaluation Dataset \n\n**Link**\n- [LINEMOD](https://bop.felk.cvut.cz/datasets/): Provides additional ground-truth annotations for all modeled objects in one of the test sets from LM. This introduces challenging test cases with various levels of occlusion\n- [YCB-V (YCB-Video)](https://bop.felk.cvut.cz/datasets/): 21 YCB objects captured in 92 videos.\n\n**Data Collection Method by Dataset**\n- Human\n\n**Labeling Method by Dataset**\n- Human\n\n**Dataset Licenses**\n- [LINEMOD](https://bop.felk.cvut.cz/datasets/): CC BY 4.0\n- [YCB-V (YCB-Video)](https://bop.felk.cvut.cz/datasets/): MIT\n\n### Evaluation Results\n\nAccuracy was determined using the following metrics:\n- Area under the curve (AUC) of ADD and ADD-S.\n- Recall of ADD that is less than 0.1 of the object diameter (ADD-0.1d).\n\nIn all evaluation, it always uses the same trained model and configurations for inference without any fine-tuning. The following table presents the results among RGBD methods on 3 core datasets from BOP. These involve various challenging scenarios (dense clutter, multi-instance, static or dynamic scenes, table-top or robotic manipulation), and objects with diverse properties (textureless, shiny, symmetric, varying sizes).\n\n| Method | Unseen objects | Occluded-LINEMOD | TLESS |YCB-Video | Mean|\n| ------- | ------- | -------| -------| -------| -------| \n| FoundationPose | ✓ | 78.8 |83.0| 88.0 | 83.3 |\n\n\n### Inference\n\n**Engine**\n- Tensor(RT)\n\n**Test Hardware [Name the specific test hardware model]**\n- A2\n- A30\n- DGX H100\n- DGX A100\n- DGX H100\n- JAO 64GB\n- Jetson AGX Xavier\n- L4\n- L40\n- NVIDIA T4\n- Orin\n- Orin Nano 8GB\n- Orin NX\n- Orin NX16GB\n- T4\n- Xavier NX\n\nThe inference performance of the provided FoundationPose model is evaluated at FP16 precisions. The model's input resolution is 6x160x160 pixels. The performance assessment was conducted using **trtexec** on a range of devices. In the table, \"BS\" stands for \"batch size.\"\n\nThe performance data presented pertains solely to model inference. The end-to-end performance, when integrated with streaming video data, the pre-processing and the post-processing, might different due to potential bottlenecks in hardware and software.\n\n| Models (FP16) | Devices | Latency (BS=1)| Images per Second (BS=1)| Latency (BS=252) | Images per Second (BS=252)| \n| ---- | ---- | ---- | ---- | ---- | ---- | \n| FoundationPose - Refine Network | Orin Nano 8GB | 4.12 | 242.59 | 1118.49 | 228.88 | \n| FoundationPose - Refine Network | Orin NX 16GB | 2.85 | 350.39 | 768.46 | 333.13 | \n| FoundationPose - Refine Network | Orin AGX 64GB | 1.10 | 908.91 | 300.26 | 852.59 |\n| FoundationPose - Refine Network | Tesla T4 | 5.62 | 182.74 | 1236.00 | 205.66 | \n| FoundationPose - Refine Network | A30 |2.55 | 392.99 | 529.99 | 475.48 | \n| FoundationPose - Refine Network | A2 |9.15 | 109.28 | 1985.32 | 126.93 | \n| FoundationPose - Refine Network | A100 |1.57 | 638.29 | 266.14 | 949.55 |\n| FoundationPose - Refine Network | H100 |1.16 | 878.56 | 123.09 | 2050.85 |\n| FoundationPose - Refine Network | L4 |2.59 | 389.66 | 558.35 | 457.71 |\n| FoundationPose - Refine Network | L40 |1.05 | 978.63 | 222.20 | 1145.97 |\n\n| Models (FP16) | Devices | Latency (BS=252)| Images per Second (BS=252)|\n| ---- | ---- | ---- | ---- | \n| FoundationPose - Score Network | Orin NX 8GB | 816.39 | 308.68 | \n| FoundationPose - Score Network | Orin NX 16GB | 564.27 | 446.59 | \n| FoundationPose - Score Network | Orin AGX 64GB | 210.02 | 1199.89 | \n| FoundationPose - Score Network | Tesla T4 | 1122.12 | 224.57 |\n| FoundationPose - Score Network | A30 |394.69 | 638.48 |\n| FoundationPose - Score Network | A2 |1702.51 | 148.55 |\n| FoundationPose - Score Network | A100 |195.37 | 1289.84 |\n| FoundationPose - Score Network | H100 |109.66 | 2301.73 |\n| FoundationPose - Score Network | L4 |470.54 | 539.91 |\n| FoundationPose - Score Network | L40 |196.02 | 1313.98 |\n\n### Output Image\n\n| **Input Image** | **Input CAD Model** | **Output Result** | \n| ------ | ------ |------ |\n|

|\n|

|\n\n## Limitations \n\n### Reflective Surface Objects\n\nThe FoundationPose might difficult to detect and track the object pose that appears reflective surface in different light conditions.\n\n## Inference Method\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 [TensorRT](https://developer.nvidia.com/tensorrt).\n\nThe primary application of these models is to estimate an object's pose from a single RGBD image or a sequence of RGBD video. They can identify the objects in photos, given the right image pre-processing and post-processing procedures.\n\nFurthermore, these models are designed for deployment to edge devices using the TensorRT. [TAO Triton apps](https://github.com/NVIDIA-AI-IOT/tao-toolkit-triton-apps) offers capabilities to construct efficient image analytic pipelines. These pipelines can capture, decode, and process data before executing inference.\n\n### Instructions to Deploy the Model with Triton Inference Server\n\nTo create the entire end-to-end inference application, deploy this model with [Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server). NVIDIA Triton Inference Server is an open-source inference serving software that helps standardize model deployment and execution and delivers fast and scalable AI in production. Triton supports direct integration of this model into the server and inference from a client.\n\nTo deploy this model with [Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server) and end-to-end inference from images, please refer to the [TAO Triton apps](https://github.com/NVIDIA-AI-IOT/tao-toolkit-triton-apps). \n\n## Ethical Considerations \n\nNVIDIA FoundationPose model estimates the object pose. However, no additional information such as people and other distractors in the background are inferred.\nThe training and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies.\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","FoundationPose",["D",1731434086421],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/foundationpose","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: | None of the Above |\n| Measures taken to mitigate against unwanted bias: | Not Applicable.|","explainability","| Field | Response |\n| -- | -- |\n| Intended Application(s) & Domain(s): | G6-DoF object pose estimation and tracking, providing the object pose and 3D bounding box. |\n| Model Type: | Estimation Object 6-Degree of Freedom Poses |\n| Intended Users: | This model is intended for the developers that estimate the position and orientation of an object in a scene. |\n| Output: | Position and Orientation of an object (6-DoF) |\n| Describe how the model works: | 1. Render the CAD model to get different poses of the object. 2. Match the testing image with the rendered image and find the most matching pose as the output. |\n| Technical Limitations: | This model may not work well for detecting partially or fully occluded objects. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Area under the curve (AUC) of ADD and ADD-S |\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 (PII)? | None |\n| Protected classes used to create this model? | Not Applicable (No PII) |\n| Was consent obtained for any PII used? | Not Applicable (No PII) |\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 PII collected for the development of the model, was it collected directly by NVIDIA? |Not Applicable |\n| If PII 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 PII 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? | Not Applicable |\n| Applicable NVIDIA Privacy Policy\t| [https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/) |","safetyAndSecurity","| Field | Response |\n| -- | -- |\n| Model Application(s): | 6-DoF Object pose Estimation and Tracking (Retail, General Purpose) |\n| Describe the life-critical application (if present). | None |\n| Use Case Restrictions: | The model may fail to give accurate three-dimensional (3D) bounding boxes and 6-DoF object poses. |\n| Describe access restrictions (if any): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. |","featureLabels",[],"displayLabels",[188,189,190,191,192,193],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_152":210,"_195":211,"_196":212,"_198":199},{"_152":208,"_195":208,"_196":209,"_198":199},{"_152":206,"_195":206,"_196":207,"_198":199},{"_152":204,"_195":204,"_196":205,"_198":199},{"_152":202,"_195":202,"_196":203,"_198":199},{"_152":194,"_195":194,"_196":197,"_198":199},"TAO Toolkit","value","filter",{"_200":201,"_195":194},"category","label","field","framework","TAO",{"_200":199,"_195":202},"NSPECT-FY5H-V6CO",{"_200":199,"_195":204},"Deep Learning",{"_200":199,"_195":206},"CV",{"_200":199,"_195":208},"Computer Vision","soln_computer_vision",{"_200":213,"_195":211},"technology","appConfig",{"_1391":1392,"_248":1393},"ngcApps",[1344],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1325":1326,"_1327":1328,"_1329":1330,"_1331":1332,"_1333":226,"_76":253,"_1334":1335,"_1336":44,"_1337":87,"_1338":87,"_1339":1340,"_1341":87,"_1342":1343},"amplitude",{"_1322":1323,"_925":-7,"_1324":20},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1316":1317,"_1318":1319,"_1320":1321},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","https://api.ngc.nvidia.com","ngcHelmOrigin","https://helm.ngc.nvidia.com","origin","https://catalog.ngc.nvidia.com","user","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1200":110,"_1201":1202,"_1203":1204,"_1205":1206},"announcements",[],"consentState",{"_290":1190,"_1191":87,"_1192":20,"_1193":20,"_1194":1195},"consentConfig",{"_272":273,"_274":275,"_276":277,"_278":279,"_280":281,"_282":283,"_284":285,"_286":287,"_288":289,"_290":291},"showContactAdmin",1784270250944,[],[],[267,268,269,270,271],{"_33":47,"_35":55,"_37":49,"_39":-5,"_41":-5,"_43":44},{"_33":47,"_35":51,"_37":49,"_39":-5,"_41":-5,"_43":44},{"_33":47,"_35":48,"_37":49,"_39":-5,"_41":-5,"_43":44},{"_33":45,"_35":46,"_37":49,"_39":-5,"_41":-5,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_43":44},"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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