Past Proposals

Pre-project Proposal

• Title: Visualizing CLARITY and Diffusion MRI data 

• Activities: 

  • Download and process CLARITY image dataset.

  • Find new diffusion MRI datasets. 

  • Add information about CLARITY and diffusion MRI datasets to the wiki.  

  • Add and compare software for visualizing these formats.

  • Learn how to convert CLARITY imaging data to volume render.  

  • Install Unity and download necessary software to support VR visualization in Unity.  

  • Convert diffusion MRI data to volume renders and visualize in HTC Vive.

  • Port CLARITY imaging renders to the YURT?

  • Comparison between Paraview, MinVR, and VTK. 

• Milestones:  

  • 2/12: Convert CLARITY imaging data to a usable format and render data in Paraview. 

  • 2/14: Add volume renders to Unity and build a steam VR app to visualize data in HTC Vive.

  • 2/21: Finish VR visualization and get user feedback from fellow classmates. 

• Deliverables: 

  • New wiki entries on CLARITY imaging and diffusion MRI imaging. 

  • An app which displays a high resolution volume rendering of CLARITY image data. 

  • A survey on people's reactions to CLARITY imaging.  

  • Comparisons of different neuroscience imaging software, in terms of speed and ease of use. 

New Pre-project Plan

• Title: Visualizing Ecology LiDAR data in Virtual Reality and YURTs 

• Activities: 

  • Examine Kellner lab and determine possible visualization methods for data.

  • Compare and contrast various LiDAR data processing tools and libraries; in terms of usability, installation time, and rendering quality. 

  • Explore different mesh rendering techniques for visualizing ecology data. 

  • Document process of rendering LiDAR data in VR and add a possible tutorial to the wiki. 

  • Explore methods for mapping LiDAR data into a continuous visualization; in other words, determine how to stitch LiDAR data into a cohesive visualization. 

  • Create a tutorial for MinVR and DinoYURT. 

• Milestones:

  • 2/12/2019, Research Kellner Lab. 

  • 2/14/2019, Convert LAS files to readable format for DinoYURT. 

  • 2/21/2019, Finalize data conversion process, if necessary, and begin HTC Vive Visualization. 

  • 2/26/2019, Finish HTC Vive Visualization. 

  • 2/28/2019, Visualize preliminary ecology data in YURT. 

  • 3/5/2019, Explore new LiDAR ecology samples from dataset. 

  • 3/7/2019, Render new ecology samples in YURT. 

  • 3/12/2019, Research algorithms for stitching ecology samples together.

  • 3/14/2019, Continue exploration of stitching together.

  • 3/19/2019, Implement a simple algorithm for stitching N ecology samples. 

  • 3/21/2019, Render stitched images in the YURT. 

  • 4/2/2019, Publish comparison between MinVR and Paraview for rendering LiDAR data.

• Deliverables: 

  • A VR visualization of LiDAR ecology data using the HTC Vive.

  • Comparisons of various LiDAR visualization and processing software.

  • Tutorials on MinVR and developing software for the YURT. 

• In-Class Activities:

  • Exploring volumetric rendering with LiDAR data.

    • Develop a short activity where we explore techniques for rendering and visualizing LiDAR data. We would most likely use Unity to visualize this data. 

Final Proposal

• Title: Visualizing Ecological LiDAR data in Virtual Reality and YURTs 

• Objective: Document and explore how MinVR and Paraview can be used to visualize composed ecological LiDAR data.

• Remark: A composed LiDAR model is several .las files stitched together based on a metric (e.g. GPS Time).   

• Questions:

  • How can LiDAR data be meaningfully composed? In other words, what metrics can we use to perform this task?

  • What software is available for visualizing LiDAR files? 

  • What are the distinctions between MinVR and Paraview for visualizing LiDAR data in VR / YURTs? 

  • What scientific insights can be derived from visualizing ecology data in VR / YURTs? 

• Activities: 

  • Examine Kellner lab and determine possible visualization methods for data.

  • Compare and contrast various LiDAR data processing tools and libraries; in terms of usability, installation time, and rendering quality. 

  • Explore different mesh rendering techniques for visualizing ecology data. 

  • Document process of rendering LiDAR data in VR and add a possible tutorial to the wiki. 

  • Explore methods for mapping LiDAR data into a continuous visualization; in other words, determine how to stitch LiDAR data into a cohesive visualization.  

  • Add pixel-wise coloring to LiDAR data based on point height and other metrics. 

  • Determine correct normalization factors for ecology data. 

  • Learn more about LiDAR data and in order to motivate composing multiple LiDAR files. 

  • Read MinVR documentation and review OpenGL. 

  • Install Paraview LiDAR plugin.

  • Install MinVR on graphics lab computer.  

  • Read papers on LiDAR stitching. 

  • Create support code for in-class activity. 

  • Download new LiDAR samples from repository. 

  • Create a tutorial on utilizing Laspy for .las data processing.

• Milestones:

  • 2/12/2019, Research Kellner Lab. 

  • 2/14/2019, Convert LAS files to readable format for DinoYURT. 

  • 2/21/2019, Finalize data conversion process, if necessary, and begin YURT Visualization of the first three LiDAR models. 

  • 2/26/2019, Finish YURT visualization for first three LiDAR models.

  • 2/28/2019, Download and visualize a new batch of LiDAR ecology models (at least N=5). 

  • 3/5/2019, Visualize all of the previous LiDAR models in VR (HTC Vive).  

  • 3/7/2019, Create a tutorial for visualizing .out files in the YURT using MinVR. 

  • 3/12/2019, Create a composite LiDAR model, composed of three separate .las files, and visualize it in the YURT. 

  • 3/14/2019, Create a tutorial on composing/stitching .las files and visualizing the results using Paraview / MinVR

  • 3/19/2019, Create a script for composing N .las files based on GPS time.  

  • 3/21/2019, Compose  N > 10 .las files and visualize the results in the YURT.  

  • 4/2/2019, Publish in-depth comparison between MinVR and Paraview for rendering and visualizing LiDAR data in the YURT .

• Deliverables: 

  • A VR visualization of LiDAR ecology data using the HTC Vive and tutorials to accompany this process.   

  • Comparisons of Paraview, Lidarview, and MinVR for visualizing and rendering LiDAR data. 

  • Scripts / tutorials on composing .las files arbitrarily and based on GPS time. 

  • A YURT visualization of composed LiDAR data. 

• In-Class Activities:

  • .las file conversion and visualization: 

    • Everyone downloads and converts one .las file in the repository to a .out file, then we will display their results in the YURT. 

    • This will visualize novel .las files, test the rigidity of my data conversion pipeline, and provide a comparison between Paraview and MinVR for visualizing LiDAR data. 

    • This might be an interesting collaboration opportunity with Ronald; he could provide a Paraview tutorial, and I would provide a MinVR tutorial.