Advancing Smart Computing: A Comprehensive Tutorial to 3D Point Clouds from Installation to Efficient Processing and Context Recognition for Next-Gen Applications

3D point clouds from LiDAR sensors have emerged as a powerful representation for understanding and interacting with the surrounding environment in smart computing systems. This tutorial aims to provide an overview of fundamental techniques, considerations, and applications of 3D point cloud recognition, with a focus on both deep learning and nondeep learning approaches. We will begin by introducing the unique characteristics and challenges of point cloud processing, highlighting the differences from traditional image-based approaches. Through the example of PointNet [1], a pioneering deep learning model for point cloud recognition, we will discuss the specific considerations and best practices for handling point cloud data. Recognizing the limitations of deep learning in resource-constrained and mobile environments, we will also explore alternative statistical and probabilistic techniques, such as Fisher Vector-based approaches, which enable efficient and lightweight point cloud processing and context recognition. These techniques are particularly relevant for smart computing applications that require real-time processing on edge devices and in Internet of Things scenarios.