tao-train-pointpillars

To train a 3D object detection model from LiDAR point clouds, you can use this Skill to train PointPillars models from scratch or resume from a checkpoint. It handles point cloud data preprocessing, model training, and evaluation for autonomous driving workflows.

Yes, you can export a trained PointPillars model to ONNX format for inference. This Skill supports exporting models directly to ONNX, enabling deployment of your 3D object detection models in production environments.

You can prune a PyTorch model to reduce its size and improve inference speed using the built-in pruning feature. After pruning your 3D object detection model, you can also retrain it to recover performance.

Yes, you need PyTorch, torchvision, and torchgeometry installed to train PointPillars models. These dependencies are required for handling point cloud data preprocessing, model training, and evaluation within the workflow.

The best way to evaluate a 3D object detection model is by running the evaluation command with a specified checkpoint. This Skill allows you to assess model performance directly from your saved checkpoints during development.

You can use this workflow for both robotics applications and autonomous driving. The Skill streamlines training and evaluation of 3D object detection models from LiDAR point clouds, applying to any robotics workflows requiring spatial perception.