A foundational dive into 3D vision, this project served as my hands-on introduction to processing point clouds with Open3D. I focused on core skills like plane detection, segmentation, and understanding 3D transformations from the ground up.

3D reconstruction from stereo images using epipolar geometry.

Semester project for my 3D Computer Geometry & Vision course.

A high-performance implementation of 3D RANSAC (Random Sample Consensus) algorithm using PyTorch and CUDA.

Parallel RANSAC for plane detection for multiple point clouds using Python and CUDA

🌐 Explore 3D computer vision with Open3D and RealSense for point cloud processing, plane detection, and real-time object detection.

Put objects on a plane. Use depth camera to find them and add label (for training Yolo).

[CVPR 2024] AirPlanes: Accurate Plane Estimation via 3D-Consistent Embeddings

A python node to detect planes from depth image by using RANSAC algorithm. Input/Output from/to ROS topics.

A 3D plane detection approach using PointNet

A python tool for fitting primitives 3D shapes in point clouds using RANSAC algorithm