This advanced 3D computer vision course focuses on building real-world pipelines for spatial data processing, point cloud analysis, and medical imaging. Learners begin with medical image registration using SimpleITK to align 3D CT and MRI volumes for accurate comparison and analysis. The course explores geometric modeling techniques such as signed distance fields and collision analysis using Open3D and PyVista. Students then dive into stereo vision reconstruction pipelines, transforming multi-view images into 3D structures. Advanced modules introduce point cloud registration with RANSAC and ICP algorithms, surface reconstruction, and non-rigid alignment techniques like coherent point drift. Deep learning applications are covered through point cloud segmentation with PointNet accelerated using OpenVINO. The series also discusses modern AI trends in point cloud perception inspired by autonomous systems research. Through practical Python implementations, learners gain hands-on experience designing scalable 3D vision pipelines for robotics, medical imaging, spatial analytics, and intelligent automation. This course is ideal for developers and researchers seeking advanced skills in modern 3D