Fundamentals of 3D Geometric Data and Computer Vision

Understanding how computers represent, process, and interpret three-dimensional space is essential for modern spatial computing, robotics, and computational vision. This text-based course equips you with the foundational principles and algorithmic concepts needed to work effectively with 3D spatial data. What you will learn: Understand fundamental 3D data structures including point clouds, polygonal meshes, and volumetric representations. Learn how 3D geometric information is captured using depth sensors, LiDAR, and multi-view techniques. Master core geometry processing algorithms such as surface reconstruction, spatial filtering, and point set registration. Apply mathematical concepts for 3D coordinate transformations and spatial feature extraction. Explore contemporary approaches to neural implicit representations and modern 3D scene analysis. Practice manipulating geometric data using written code snippets and structural walkthroughs. The material begins with essential terminology and geometric foundations before progressing systematically into practical processing workflows and analytical techniques. This course is designed for beginners interested in spatial computing and computer vision, requiring no prior experience with 3D data. Begin reading today to build a solid grasp of 3D geometric processing and spatial algorithms.