The models described in this card detect one or more objects from a LIDAR point cloud file and return a 3D bounding box around each object. These pre-trained PointPillars models are trained on a point cloud dataset collected by a solid state LIDAR.
These models are based on PointPillars architecture in NVIDIA TAO Toolkit.
The training algorithm optimizes the network to minimize the localization and confidence loss for the objects.
The PointPillars models were trained on a proprietary LIDAR point cloud dataset.
The evaluation dataset for the PointPillars models is obtained through the same way as training dataset.
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.
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).
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:
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.
deployable models are intended for easy deployment to the edge using TensorRT.
deployable models are encrypted and can be decrypted with the following key:
Please make sure to use this as the key for all TAO commands that require a model load key.
The models has 2 inputs.
Category labels(Vehicle, Pedestrian, Cyclist) and 3D bounding-box coordinates for each detected objects in the input point cloud file.
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.
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.
Install TensorRT 8.2 or use pre-installed one if it is already installed.
Install TensorRT OSS 22.02.
git clone -b 22.02 https://github.com/NVIDIA/TensorRT.git TensorRT cd TensorRT git submodule update --init --recursive mkdir -p build && cd build cmake .. -DCUDA_VERSION=$CUDA_VERSION -DGPU_ARCHS=$GPU_ARCHS make nvinfer_plugin -j$(nproc) cp libnvinfer_plugin.so.8.2.* /usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so.8.2.3 cp libnvinfer_plugin_static.a /usr/lib/x86_64-linux-gnu/libnvinfer_plugin_static.a
Train the model in TAO Toolkit and export to the
Generate TensorRT engine on target device with
tao-converter -k $KEY \ -e $USER_EXPERIMENT_DIR/trt.fp16.engine \ -p points,1x204800x4,1x204800x4,1x204800x4 \ -p num_points,1,1,1 \ -t fp16 \ pointpillars_deployable.etlt
cd ~ git clone https://github.com/NVIDIA-AI-IOT/tao_toolkit_recipes.git cd tao_toolkit_recipes git lfs pull cd tao_pointpillars/tensorrt_sample/test mkdir build cd build cmake .. -DCUDA_VERSION=<CUDA_VERSION> make -j8 ./pointpillars -e /path/to/tensorrt/engine -l ../../data/102.bin -t 0.01 -c Vehicle,Pedestrain,Cyclist -n 4096 -p -d fp16
Currently the TensorRT engine of PointPillars model can only run at batch size 1.
License to use these models is covered by the Model EULA. By downloading the trainable or deployable version of the model, you accept the terms and conditions of these licenses.
NVIDIA PointPillars model detects 3D objects in a point cloud file.
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.