AIIRA 3D Reconstruction Pipeline for Maize Phenotyping
Converting drone- and phone-recorded RGB videos of maize tassels into structured 3D point-cloud outputs for agricultural phenotyping.
01 / Overview
I am working on an agricultural AI research project focused on converting drone- and phone-recorded RGB videos of maize tassels into 3D point-cloud outputs. The project supports maize phenotyping by using computer vision and 3D reconstruction methods to move from raw field video to structured spatial representations of plants.
02 / Technical focus
This project combines applied computer vision, 3D reconstruction, agricultural AI, field data collection, and research data engineering. A key part of the work is making the pipeline organized and reproducible across different recording sources, including drone and phone footage.
03 / Pipeline
- Raw RGB video01
- Frame extraction02
- COLMAP camera pose estimation03
- NeRF / Nerfstudio training04
- Point-cloud export05
- Tassel-level .ply extraction06
04 / What I worked on
- Building computer vision pipelines for agricultural phenotyping using RGB video collected from field environments.
- Processing drone and phone recordings into extracted frames for reconstruction workflows.
- Supporting Structure-from-Motion pipelines using COLMAP for feature extraction, feature matching, geometric verification, triangulation, and camera pose estimation.
- Running NeRF and Nerfstudio workflows to reconstruct 3D representations from 2D frames and estimated camera poses.
- Organizing data-processing stages across raw video, preprocessing, pose estimation, NeRF training, point-cloud export, and individual tassel extraction.
- Preparing point-cloud outputs and .ply files for downstream analysis of individual maize tassels.
05 / Tools and topics