3D point clouds provide us with detailed and precise information about any environment thanks to the use of LIDAR scanners. The use of artificial intelligence over point clouds allows us to create a digital twin.

In this session, we will introduce the point cloud concept and explain in detail the current state of the art of different artificial intelligence techniques to object detection and segmentation.

Point cloud datasets have a million points and are difficult to process. For this reason, the most efficient encoder for object detection will be used: CUDA-Point pillars. This model has a good performance to make inferences in IoT devices in real-time.

A real case about pipes detection (in industrial plants) will be shown. All the deep learning workflow will be explained step by step: from training (with Pytorch) to model optimization and quantization (with tensorRT). This demo will be run in an Nvidia Jetson nano.