Improved Plane Detection in 3D Reconstruction from 2D Images

3D reconstruction is a fundamental technology with applications in autonomous driving, virtual reality, and game development.Plane detection is a critical component in 3D reconstruction, as planes form the structural backbone of most environments.However, existing plane detection methods have limitations in their accuracy, while machine learning based plane detection is largely limited by its training dataset.This research aims to enhance plane detection in 3D reconstruction from 2D images by integrating monocular depth estimation, point cloud generation, RANSAC, and clustering techniques to better detect and reconstruct planes.In this project, I generated depth maps from 2D images by implementing monocular depth generation.Next, I then utilized pyRANSAC, a plane detection algorithm to detect basic planes.I then improved the algorithm to detect multiple planes and used a clustering algorithm to address the many artifacts induced by the algorithm.My proposed method was compared with existing approaches to show that my method has a more robust and more accurate plane detection.