PointPillars Model Card

Description:

PointPillarNet detects objects from a LIDAR point cloud file. This model is ready for commercial use.

References:

Using TAO Pre-trained Models

Get TAO Container
Get other purpose-built models from the NGC model registry:\n
- TrafficCamNet
- PeopleNet
- PeopleNet-Transformer
- DashCamNet
- FaceDetectIR
- VehicleMakeNet
- VehicleTypeNet
- PeopleSegNet
- PeopleSemSegNet
- License Plate Detection
- License Plate Recognition
- PoseClassificationNet
- Facial Landmark
- FaceDetect
- 2D Body Pose Estimation
- ActionRecognitionNet
- People ReIdentification
- PointPillarNet
- CitySegFormer
- Retail Object Detection
- Retail Object Embedding
- Optical Inspection
- Optical Character Detection
- Optical Character Recognition
- PCB Classification
- PeopleSemSegFormer
\n

Model Architecture:

Architecture Type: Convolution Neural Network (CNN)
\nNetwork Architecture: PointPillars Architecture

Input:

Input Type(s): Point Cloud File
\nInput Format(s): Lidar
\nInput Parameters: Points, Num_Points
\nOther Properties Related to Input:

Points have the shape (N, P, 4), where N is the batch size, P is the maximum number of points in a point cloud file in the dataset., and N is the number of features per point.
Num_Points is the actual number of points in each point cloud file. It has the shape (N), where N is the batch size as above.

Output:

Output Type(s): Label(s), Bounding-Box(es), Confidence Scores
\nOutput Format: Label: Text String(s); Bounding Box: (x-coordinate, y-coordinate, z-coordinate, width, height, depth), Confidence Scores: Floating Point
\nOther Properties Related to Output: Category Label(s): (Vehicle, Pedestrian, Cyclist); Bounding Box Coordinates; Confidence Scores

Software Integration:

Runtime Engine(s):

TAO - 5.2
DeepStream 6.1 or later

Supported Hardware Architecture(s):

Ampere
Jetson
Hopper
Lovelace
Pascal
Turing
Volta

Supported Operating System(s):

Linux
Linux 4 Tegra

Model Version(s):

deployable_v1.0
trainable_v1.0

Training & Evaluation:

Training Dataset:

Data Collection Method by dataset:

Automatic/Sensors

Labeling Method by dataset:

Human

Properties:
\n7481 training images of 80,256 labeled objects from a proprietary LIDAR point cloud dataset of vehicles, pedestrians, and cyclists, and other elements of road scenery collected by a solid state LIDAR.

Methodology and KPI

The key performance indicator is the mean average precision(mAP) object detection in 3D or Bird's-Eye View(BEV). The KPI for the evaluation data are reported in the table below.

\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	Dataset	mAP BEV/3D
`pointpillars_trainable.tlt`	proprietary dataset	65.2167%/51.7159%
`pointpillars_deployable.etlt`	proprietary dataset	66.6860%/52.8530%

Evaluation Dataset:

Data Collection Method by dataset:

Automatic/Sensors

Labeling Method by dataset:

Human

Properties:
\n7581 training images of 80,256 labeled objects from a proprietary LIDAR point cloud dataset of vehicles, pedestrians, and cyclists, and other elements of road scenery collected by a solid state LIDAR.

Inference:

Engine: Tensor(RT)
\nTest Hardware:

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

The inference is run on the provided deployable models at FP16 precision. The Jetson devices run at Max-N configuration for maximum system performance. The performance shown below is only for inference of the usa deployable(pruned) model. As a comparison, we also show the inference performance of the unpruned model(not available here).

\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\">

Model	Device	Precision	Batch_size	FPS
Pruned	Xavier	FP16	1	39
Unpruned	Xavier	FP16	1	31

How to use this model

These models need to be used with NVIDIA Hardware and Software. For Hardware, the models can run on any NVIDIA GPU including NVIDIA Jetson devices. These models can only be used with Train Adapt Optimize (TAO) Toolkit, or TensorRT.

Primary use case intended for these models is detecting objects in a point cloud file.

Totally there are two models provided:

pointpillars_trainable.tlt
pointpillars_deployable.etlt

The trainable models are intended for training and fine-tuning using TAO Toolkit along with the user's dataset of point cloud. High fidelity models can be trained and adapted to the use case.

The usa deployable models are intended for easy deployment to the edge using TensorRT.

The trainable and deployable models are encrypted and can be decrypted with the following key:

Model load key: tlt_encode

Please make sure to use this as the key for all TAO commands that require a model load key.

Instructions to use trainable model with TAO Toolkit

In order to use these models as a pre-trained model for transfer learning, please use the snippet below as template for the OPTIMIZATION component of the config file to train a PointPillars model. For more information on the config file, please refer to the TAO Toolkit User Guide.

Set PRETRAINED_MODEL_PATH in OPTIMIZATION parameter

  PRETRAINED_MODEL_PATH: \"/path/to/the/model.tlt\"\n

Instructions to deploy these models with TensorRT

PointPillars model can be deployed in TensorRT with the TensorRT C++ sample with TensorRT 8.2.

As a dependency, the TensorRT sample requires the TensorRT OSS 22.02 to be installed.

Detailed steps are shown below.

\n
Install TensorRT 8.2 or use pre-installed one if it is already installed.
\n
\n
Install TensorRT OSS 22.02.
\n

git clone -b 22.02 https://github.com/NVIDIA/TensorRT.git TensorRT\ncd TensorRT\ngit submodule update --init --recursive\nmkdir -p build && cd build\ncmake .. -DCUDA_VERSION=$CUDA_VERSION -DGPU_ARCHS=$GPU_ARCHS\nmake nvinfer_plugin -j$(nproc)\ncp libnvinfer_plugin.so.8.2.* /usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so.8.2.3\ncp libnvinfer_plugin_static.a /usr/lib/x86_64-linux-gnu/libnvinfer_plugin_static.a\n

\n
Train the model in TAO Toolkit and export to the .etlt model.
\n
\n
Generate TensorRT engine on target device with tao-converter.
\n

tao-converter  -k $KEY  \\\n               -e $USER_EXPERIMENT_DIR/trt.fp16.engine \\\n               -p points,1x204800x4,1x204800x4,1x204800x4 \\\n               -p num_points,1,1,1 \\\n               -t fp16 \\\n               pointpillars_deployable.etlt\n

Clone, build and run the C++ sample.

\nmake -j8\n./pointpillars -e /path/to/tensorrt/engine -l ../../data/102.bin -t 0.01 -c Vehicle,Pedestrain,Cyclist -n 4096 -p -d fp16\n\n\">

cd ~\ngit clone https://github.com/NVIDIA-AI-IOT/tao_toolkit_recipes.git\ncd tao_toolkit_recipes\ngit lfs pull\ncd tao_pointpillars/tensorrt_sample/test\nmkdir build\ncd build\ncmake .. -DCUDA_VERSION=<CUDA_VERSION>\nmake -j8\n./pointpillars -e /path/to/tensorrt/engine -l ../../data/102.bin  -t 0.01 -c Vehicle,Pedestrain,Cyclist -n 4096 -p -d fp16\n\n

Technical blogs

Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - Part 1 | Part 2
Learn how to train real-time License plate detection and recognition app with TAO and DeepStream.
Model accuracy is extremely important, learn how you can achieve state of the art accuracy for classification and object detection models using TAO
Learn how to train Instance segmentation model using MaskRCNN with TAO
Learn how to improve INT8 accuracy using Quantization aware training(QAT) with TAO
Read the technical tutorial on how PeopleNet model can be trained with custom data using Transfer Learning Toolkit
Learn how to train and deploy real-time intelligent video analytics apps and services using DeepStream SDK

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++ Promise and the Explainability, Bias, Safety & Security, and Privacy Subcards.

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\n**Network Architecture:** PointPillars Architecture
\n\n## Input:\n**Input Type(s):** Point Cloud File
\n**Input Format(s):** Lidar
\n**Input Parameters:** Points, Num_Points
\n**Other Properties Related to Input:**
\n- Points have the shape (N, P, 4), where N is the batch size, P is the maximum number of points in a point cloud file in the dataset., and N is the number of features per point.
\n- Num_Points is the actual number of points in each point cloud file. It has the shape (N), where N is the batch size as above.\n\n## Output:\n**Output Type(s):** Label(s), Bounding-Box(es), Confidence Scores
\n**Output Format:** Label: Text String(s); Bounding Box: (x-coordinate, y-coordinate, z-coordinate, width, height, depth), Confidence Scores: Floating Point
\n**Other Properties Related to Output:** Category Label(s): (Vehicle, Pedestrian, Cyclist); Bounding Box Coordinates; Confidence Scores
\n\n## Software Integration:\n**Runtime Engine(s):** \n* TAO - 5.2
\n* DeepStream 6.1 or later
\n\n**Supported Hardware Architecture(s):**
\n* Ampere
\n* Jetson
\n* Hopper
\n* Lovelace
\n* Pascal
\n* Turing
\n* Volta
\n\n**Supported Operating System(s):**
\n* Linux
\n* Linux 4 Tegra
\n\n## Model Version(s): \n- deployable_v1.0\n- trainable_v1.0\n\n## Training & Evaluation: \n\n## Training Dataset:\n**Data Collection Method by dataset:**
\n* Automatic/Sensors
\n\n**Labeling Method by dataset:**
\n* Human
\n\n**Properties:**
\n7481 training images of 80,256 labeled objects from a proprietary LIDAR point cloud dataset of vehicles, pedestrians, and cyclists, and other elements of road scenery collected by a solid state LIDAR. \n\n### Methodology and KPI\n\nThe key performance indicator is the mean average precision(mAP) object detection in 3D or Bird's-Eye View(BEV). The KPI for the evaluation data are reported in the table below.\n\n| Model | Dataset | mAP BEV/3D |\n| ---- | ---- | ---- |\n| `pointpillars_trainable.tlt` | proprietary dataset |65.2167%/51.7159%|\n| `pointpillars_deployable.etlt` | proprietary dataset | 66.6860%/52.8530% |\n\n## Evaluation Dataset:\n**Data Collection Method by dataset:**
\n* Automatic/Sensors
\n\n**Labeling Method by dataset:**
\n* Human
\n\n**Properties:**
\n7581 training images of 80,256 labeled objects from a proprietary LIDAR point cloud dataset of vehicles, pedestrians, and cyclists, and other elements of road scenery collected by a solid state LIDAR. \n\n## Inference:\n**Engine:** Tensor(RT)
\n**Test Hardware:**
\n- Jetson AGX Xavier\n- Xavier NX\n- Orin\n- Orin NX\n- NVIDIA T4\n- Ampere GPU\n- A2\n- A30\n- L4\n- T4\n- DGX H100\n- DGX A100\n- DGX H100\n- L40\n- JAO 64GB\n- Orin NX16GB\n- Orin Nano 8GB\n\nThe inference is run on the provided deployable models at FP16 precision. The Jetson devices run at Max-N configuration for maximum system performance. The performance shown below is only for inference of the usa deployable(pruned) model. As a comparison, we also show the inference performance of the unpruned model(not available here).\n\n| Model | Device | Precision | Batch_size | FPS |\n| ---- | ---- | ---- | ---- | --- |\n| Pruned | Xavier | FP16 | 1 | 39 |\n| Unpruned | Xavier | FP16 | 1 | 31 |\n\n## How to use this model \n\nThese models need to be used with NVIDIA Hardware and Software. For Hardware, the models can run on any NVIDIA GPU including NVIDIA Jetson devices. These models can only be used with [Train Adapt Optimize (TAO) Toolkit](https://developer.nvidia.com/tao-toolkit), or [TensorRT](https://developer.nvidia.com/tensorrt).\n\nPrimary use case intended for these models is detecting objects in a point cloud file.\n\nTotally there are two models provided:\n\n- `pointpillars_trainable.tlt`\n- `pointpillars_deployable.etlt`\n\nThe `trainable` models are intended for training and fine-tuning using TAO Toolkit along with the user's dataset of point cloud. High fidelity models can be trained and adapted to the use case.\n\nThe usa `deployable` models are intended for easy deployment to the edge using TensorRT.\n\nThe `trainable` and `deployable` models are encrypted and can be decrypted with the following key:\n\n- Model load key: `tlt_encode`\n\nPlease make sure to use this as the key for all TAO commands that require a model load key.\n\n### Instructions to use trainable model with TAO Toolkit\n\nIn order to use these models as a pre-trained model for transfer learning, please use the snippet below as template for the `OPTIMIZATION` component of the config file to train a PointPillars model. For more information on the config file, please refer to the [TAO Toolkit User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\n1. Set `PRETRAINED_MODEL_PATH` in `OPTIMIZATION` parameter\n\n ```py\n PRETRAINED_MODEL_PATH: \"/path/to/the/model.tlt\"\n ```\n\n### Instructions to deploy these models with TensorRT\n\nPointPillars model can be deployed in TensorRT with the [TensorRT C++ sample](https://github.com/NVIDIA-AI-IOT/tao_toolkit_recipes/tree/main/tao_pointpillars/tensorrt_sample) with TensorRT 8.2.\n\nAs a dependency, the TensorRT sample requires the [TensorRT OSS 22.02](https://github.com/NVIDIA/TensorRT/tree/22.02) to be installed.\n\nDetailed steps are shown below.\n\n* Install TensorRT 8.2 or use pre-installed one if it is already installed.\n\n* Install TensorRT OSS 22.02.\n\n```\ngit clone -b 22.02 https://github.com/NVIDIA/TensorRT.git TensorRT\ncd TensorRT\ngit submodule update --init --recursive\nmkdir -p build && cd build\ncmake .. -DCUDA_VERSION=$CUDA_VERSION -DGPU_ARCHS=$GPU_ARCHS\nmake nvinfer_plugin -j$(nproc)\ncp libnvinfer_plugin.so.8.2.* /usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so.8.2.3\ncp libnvinfer_plugin_static.a /usr/lib/x86_64-linux-gnu/libnvinfer_plugin_static.a\n```\n\n* Train the model in TAO Toolkit and export to the `.etlt` model.\n\n* Generate TensorRT engine on target device with `tao-converter`.\n\n```\ntao-converter -k $KEY \\\n -e $USER_EXPERIMENT_DIR/trt.fp16.engine \\\n -p points,1x204800x4,1x204800x4,1x204800x4 \\\n -p num_points,1,1,1 \\\n -t fp16 \\\n pointpillars_deployable.etlt\n```\n\n* Clone, build and run the C++ sample.\n\n```\ncd ~\ngit clone https://github.com/NVIDIA-AI-IOT/tao_toolkit_recipes.git\ncd tao_toolkit_recipes\ngit lfs pull\ncd tao_pointpillars/tensorrt_sample/test\nmkdir build\ncd build\ncmake .. -DCUDA_VERSION=\nmake -j8\n./pointpillars -e /path/to/tensorrt/engine -l ../../data/102.bin -t 0.01 -c Vehicle,Pedestrain,Cyclist -n 4096 -p -d fp16\n\n```\n\n## Technical blogs \n\n- Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - [Part 1](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-1) | [Part 2](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-2)\n- Learn how to train [real-time License plate detection and recognition app](https://developer.nvidia.com/blog/creating-a-real-time-license-plate-detection-and-recognition-app) with TAO and DeepStream.\n- Model accuracy is extremely important, learn how you can achieve [state of the art accuracy for classification and object detection models](https://developer.nvidia.com/blog/preparing-state-of-the-art-models-for-classification-and-object-detection-with-tao-toolkit/) using TAO\n- Learn how to train [Instance segmentation model using MaskRCNN with TAO](https://developer.nvidia.com/blog/training-instance-segmentation-models-using-maskrcnn-on-tao-toolkit/)\n- Learn how to improve INT8 accuracy using [Quantization aware training(QAT) with TAO](https://developer.nvidia.com/blog/improving-int8-accuracy-using-quantization-aware-training-and-tao-toolkit/)\n- Read the technical tutorial on how [PeopleNet model can be trained with custom data using Transfer Learning Toolkit](https://devblogs.nvidia.com/training-custom-pretrained-models-using-tlt/)\n- Learn how to [train and deploy real-time intelligent video analytics apps and services using DeepStream SDK](https://devblogs.nvidia.com/building-iva-apps-using-deepstream-5.0/)\n\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- Read the [TAO getting Started](https://docs.nvidia.com/tao/tao-toolkit/) guide and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html).\n- If you have any questions or feedback, please refer to the discussions on [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17)\n- Deploy your model on the edge using DeepStream. Learn more about DeepStream SDK https://developer.nvidia.com/deepstream-sdk\n\n## Ethical Considerations:\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++ Promise and the Explainability, Bias, Safety & Security, and Privacy Subcards.","PointPillarNet",["D",1732745268284],"/api/image/nvidia/tao/models/pointpillarnet","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","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: | No measures taken to mitigate against unwanted bias.|","explainability","| Field | Response |\n| -- | -- |\n| Intended Application(s) & Domain(s): | Classifying cyclists, persons, and vehicles. |\n| Model Type: | Object Detection |\n| Intended Users: | 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 a LIDAR point cloud file and returns a 3D bounding box and confidence score for each object. |\n| Technical Limitations: | PointPillarNet does not give good results for very crowded traffic scenes. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Accuracy, Mean Average Precision (mAP), Recall |\n| Potential Known Risks: | Model may fail to detect classifying objects. |\n| Licensing: | [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/) |","privacy","| Field | Response |\n| -- | -- |\n| Generatable or reverse engineerable personally-identifiable information (PII)? | None |\n| Protected classes used to create this model? | None |\n| Was consent obtained for any PII used? | Yes |\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? | Yes |\n| If PII collected for the development of the model, was it collected directly by NVIDIA? | Yes, PII was collected by NVIDIA. |\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| Yes |\n| If PII collected for the development of this AI model, was it minimized to only what was required? | Yes |\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? | Yes |\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): | Model to detect one or more objects from a LIDAR point cloud file and return 3D bounding boxes. |\n| Describe the life-critical application (if present). | Potential for life-critical impact in detecting the presence of people in floor space. |\n| Use Case Restrictions: | Should not be used in \"Critical Applications;\" [reference](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/). |\n| Describe access restrictions (if any): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. 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