KD-Trees Explained – Multidimensional Search, Nearest Neighbor, and Real-World Applications

Описание: This presentation covers KD-Trees (k-dimensional trees), a data structure used for efficient multidimensional search.
I explain how KD-Trees are structured, how they perform nearest-neighbor search, and why they are widely used in graphics, machine learning, robotics, and AR/VR systems.

Topics covered:
• Motivation & context
• KD-Tree structure and dimensional splitting
• Building the tree
• Insertion and nearest-neighbor search walkthroughs
• Complexity analysis
• Pros, cons, and trade-offs
• Applications in smart glasses, SLAM, and spatial computing

This video was created as part of the CPTS 223 (Advanced Data Structures) extra credit assignment.