3D Vision Unit

This project aims to develop a more biologically informed approach for registering 3D models of anatomical structures, such as mouse skulls. Traditional registration methods (e.g., Coherent Point Drift) treat point clouds in a purely geometric way and often ignore the underlying biological constraints of how real organisms vary in shape. By incorporating statistical shape models—built from actual biological data—into the registration pipeline, the team seeks to achieve more accurate correspondences between specimens. This improvement will ultimately enhance automated measurements, morphological analyses, and potentially benefit broader applications where 3D biological data play a role, such as comparative anatomy or medical imaging.

For more information visit: https://humanaugmentedanalyticsgroup.miraheze.org/wiki/3D_Modeling

The project aims to develop innovative algorithms for processing and analyzing terrestrial LiDAR (Light Detection and Ranging) scans of natural vegetation, with the goal of creating comprehensive, open-source software packages in Python or R. This project will focus on creating novel computational methods to extract, process, and analyze complex vegetation structure data from LiDAR point clouds, enabling more accurate and automated assessment of natural vegetation characteristics.

We are especially interested in segmenting and measuring elements of branch and vegetation structure. By developing these algorithms into accessible software packages, the project will provide the scientific community with robust tools for vegetation analysis, supporting applications in ecology, forestry, and environmental monitoring.
For more information, please visit Stroud Lab and HAAG Research Collaboration.

Photogrammetry is a powerful method for turning 2D images into detailed 3D models, especially useful when working with natural objects. It works by analyzing how the same points appear across multiple overlapping photographs taken from different angles. From this, it can infer depth and recreate the shape of the object in three dimensions.
This approach is especially valuable for capturing the complex geometry and texture of things like rocks, plants, shells, or animal specimens.

With the right lighting and careful image capture, photogrammetry can preserve fine details like bark textures, leaf patterns, or surface erosion that might be lost with more abstracted scanning methods.
That said, natural objects do pose some challenges. Their surfaces often have repeating patterns, lack sharp edges, or may even move slightly during capture. Leaves blowing in the wind or translucent petals catching the light can confuse the reconstruction process. To get around this, it helps to shoot under stable lighting conditions, avoid motion, and take a lot of overlapping photos from many viewpoints.

For projects involving natural environments or organic forms, photogrammetry offers a flexible and approachable way to build realistic 3D models using just a camera and some patience.

Lead by Dr. Arthur Porto and supported by PhD student Breanna Shi. The team members include James Hennessy, Xin Lin, Clinton Kunhardt, and Caleb Wheeler.