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.