Activity Log

• 1/25/19, 7:00-8:00 AM, 1 HR, created a journal page; this took longer than I expected because I had trouble editing the site. 

• 1/26/19, 7:00-8:00 AM; 9:00-11:00 AM, 3 HR, installed Paraview; again, this took longer than I expected. The main issue was that the install instructions weren't correct for my version of Linux. So, I tried installing Paraview via apt-get, which worked but only installed an older version of Paraview. Finally, I compiled Paraview from its source code, which ended up being the best solution.    

• 1/28/19, 7:00-9:30 PM, 1 HR 30 MIN, Read the History of VR and several other articles on the wiki,  researched .las files and possible software/libraries for processing these files, and updated the description of .las files on the wiki based on my research. I also updated the wiki entry on Unity3D, including a brief tutorial on how to install Unity3D for Ubuntu. Finally, I started to install MinVR.  

• 1/30/19, 9:00-11:30 PM, 2 HR 30 MIN, Originally, I planned on working with ecology data for my project (more on that shortly), but there are some other ideas that may be interesting to explore too. While broad, topology/geometry may be fun to experiment with; for instance, consider the hypersphere packing problem; would it be easier to solve such problems if VR visualization tools were developed? Or consider Dugan Hammock, who explores the relationship between 4D geometries and quantum gravity; could VR aid exploratory learning in that domain? Another interesting domain is medicine. Medical imaging, in particular, seems to be ripe for VR visualization. For instance, consider the process of segmenting brain tumors in MRI scans; typically, this task is performed by neural networks and other algorithms; however, doctors often refine these segmentations, so would it be beneficial to visualize these brain scans as either a volume rendering or surface rendering? dicomvr is a good example of well-executed medical imaging for VR. Nevertheless, it seems that (a) VR interfaces are still too cumbersome, at least for non-technical users, and (b) VR are graphics are often quite poor. I think that AR may resolve some of these issues but its hard to tell. 

• 1/30/19, 9:00-11:30 PM (continued), Aside from project ideas, I downloaded the ecology data from the course website; however, I'm still in the process of parsing it. It's unclear what each file represents since they have names like Zofin_04162018_-744000_-1202250. I'm using PyLidar to parse the data into a VTK file, so I can visualize it in Paraview.

• 2/1/19, 10:00-11:00 PM, 1 HR, Finished building MinVR. Hopefully, it will run nicely on Linux.  

• 2/3/19, 8:00-11:00 PM, 3 HR, Tested various .las point cloud viewers. For Linux, Displaz seems to be a decent option. It is a pretty hackable piece of software; however, the process of installing it is quite painful. No .deb or binary files are provided, so you have to build it from the source, a non-trivial endeavor. In particular, Displaz requires Qt which adds a whole layer of complexity to the build process. Luckily, after reading through a couple forum posts on Github, it appears that you can install Displaz via Flatpak. Nevertheless, after booting up the program, it visualizes .las pretty easily. Also, lidarview.com is an excellent alternative. Even though its web-based, it runs on top of WebGL, so it is pretty fast! However, it's not open-source, so if you experience any issues, your pretty much out of luck. 

• 2/4/19, 8:00-10:00 PM, 2 HR, Tried installing FrugoViewer, a popular Lidar viewer; however, it's for Window only, so it didn't run on my Linux machine. Also, I'm in the process of implementing a pipeline for converting .las files to .vtk files, so we can view our lidar data in Paraview. This pipeline uses laspy for loading .las files. I'm planning on rendering the forest scene as a volume render, or maybe a surface rendering. Then, I would need to convert this model into something compatible with OpenGL and MinVR. I'm unsure what that might be. 

• 2/4/19, 8:00-10:00 PM (continued) Now, I have two project ideas I'm considering: ecology/lidar visualization in the YURT, topology/geometry visualization for VR or YURT, or something related to neuroscience. For the ecology project, I would, as I mentioned previously, build a pipeline to convert .las files into .vtk files, render the lidar data as a volume render, and render that model in OpenGL. For the topology project, I'm not quite sure what I would render, so I think that I would need to collaborate with a mathematics professor to elaborate on this idea. Now, for the neuroscience visualization, it might be interesting to visualize models created with CLARITY imaging techniques; I'm wondering if there are any neuroscience professors at Brown who would be interested in such a project. I believe that neuroscience would be an interesting domain for VR visualization since the brain and its functionality are inherently volumetric. I will follow up on the last idea.   

• 2/5/19, 7:00 - 10:00 PM, 3 HR, Some updates regarding visualizing CLARITY image data. Several labs have released CLARITY data, but Diesseroth lab at Stanford appears to have some of the most accessible datasets. In particular, I'm working with an image of a mouse brain. The CLARITY scan is stored as a collection of .tif files, so I need to convert these individual images into a volume/surface rendering. I'm not sure how exactly to do that, but I'm working on a solution. Also, I plan to add a page to the wiki on CLARITY images. 

• 2/6/19, 8:00 - 11:00 PM, 3 HR, I'm still debating on whether to work with diffusion MRI data / CLARITY image data or  las files and attempt to perform some manifold learning visualization (need to follow up on this). Since the former is more concrete, I will draft a pre-project plan using that idea.  

• 2/10/19, 2:00 - 4:20 PM, 2 HR 20 MIN, took notes on the Kellner lab for forest project. See https://docs.google.com/document/d/1anwhZOJ_qsJ_2lUA8I1wDGrD3M7EcKIe9dEwv2hFh-o/edit?usp=sharing. 

• 2/11/19, 8:00 - 11:00 AM, 3 HR, Studied methods for converting LAS to DinoYURT format. Conclusion: I need to work on a Windows computer.

• 2/12/19, 7:00 - 10:00 PM, 3 HR, Wrote a script file to convert LAS files to .out files! I will still need to modify it to scale to larger datasets. 

• 2/13/19, 2:00 - 3:00 PM, 10:30 - 12:30 PM, 3 HR, Finished LAS to out file conversion with normalization features. I scaled everything in the .las file to a fixed range [-1, 1]. However, I think that may be too conservative, particularly in the z-axis. Also, expect massive data files after conversion! For instance, a 2.7 GB .las file inflates to a ~10 GB .out file; conversion times are also a bit lengthy; approximately, 10 minutes on Intel i5 4300U, no multi-threading, batch write size of 1,000,000 doubles. 

• 2/14/19, 8:00 - 9:00 AM, 1 HR, Added dimensional normalization to .las conversion script and setup .config file. 

• 2/14/19, 11:45 AM - 12:45 PM,  1 HR, edited journal and added project evaluation.  

Wiki Additions

• Unity Ubuntu Installation Tutorial -- 100% complete.

• Data Types and Examples / Point Cloud Data 

  • Added a description of .las files -- 100% complete

  • Added an extensive list of software for processing and visualizing .las file -- 100% complete

  • Added a tutorial on Laspy and Python-PCL -- 100% complete; should be extensive enough for new users. 

  • Added a papers page on processing LiDAR / pointcloud data

  • Added two new data types: CLARITY imaging data and MRI imaging data -- 100% complete.

  • Added a comparison of several types of pointcloud visualization software -- 100% complete.

  • Added a link to my PointSample github repository, which contains my subsampling algorithms along with examples, and a short tutorial -- 100% complete.

• DinoYURT Tutorial -- 100% complete

Evaluations

Project Evaluation

• The proposed project clearly identifies deliverable additions to our VR Software Wiki: Agree. My project will contribute a series of tutorials on LiDAR visualization software, comparisons between Paraview and MinVR, and in-depth information on .LAS files.  

• The proposed project will inform future research, ie, advancing human knowledge: Agree. My project is targeting a particular research application: deriving biological insights from LiDAR data. So, I believe that my project with help advance the state of ecology research, particularly for the Kellner lab.  

• The proposed project involves large data visualization along the lines of the "Data Types" wiki page and identifies the specific data and software that it will use: Agree. My project specifies concrete data types, including .las files, and seeks to visualize very large LiDAR files. 

• The proposed project has a realistic schedule with explicit and measurable milestones at least each week and mostly every class: Agree. I believe that my project goals are explicit and reasonably ambitious, with milestones every day of class and week.

• The proposed project includes an in-class activity: Agree. I have included an in-class activity in my project proposal; however, it could be more concrete.

• The proposed project has resources available with sufficient documentation: Agree. I have access to all of the necessary data and programs to implement my project. 

Personal Journal Evaluation 

• Journal activities are explicitly and clearly related to course deliverables: 4, Journal entries are reasonable in length, with often explicit and detailed descriptions.  

• Deliverables are described and attributed in wiki: 4, Within each journal entry, my deliverables are clearly stated and are either relevant to the wiki and/or my project. 

• Report states total amount of time: 4, Total amount of time is clearly stated and hour-by-hour accounts are included.

• Total time is appropriate: 4, A reasonable amount of time is spent researching, programming, and adding wiki entries; however, more time could be spent working. 

Journal / Wiki Contributions Peer Review

• Name: Ronald Baker

• Wiki Additions: 

  • TinkerCAD Tutorial:

    • Well formatted, informative and detailed; lots of pictures; includes difficultly levels for different types of users.

    • Overall: Excellent tutorial!

  • Blender / LiDAR Tutorial:

    • Brief and well written; includes step by step videos to compliment tutorial (very nice!)

    • Overall: Excellent!

  • Paraview .las to .vtk Tutorial:

    • Includes a detailed tutorial but no wikipage; nevertheless, it serves its purpose. 

    • Overall: Could be improved by adding a standalone wiki page.

  • Example LiDAR data

  • Photoshop 3D Modeling tutorial:

    • Very detailed and pretty! Each step includes a high-resolution picture to complement the text; very accessible!

    • Overall: Amazing tutorial! 

  • Veloview LiDAR Tutorial:

    • Relatively technical and somewhat daunting for beginners but still well written; could use some more pictures.

    • Overall: Good tutorial on a very technical topic.

  • .pcap files added to Wiki

• Journal Evaluation: 

  • Includes project objectives, milestones, deliverables; also, Ronald's journal contains links to his past presentations and a list of software he evaluated over the semester. 

  • Well formatted and includes weekly / daily updates; could include more entries. 

  • Links to videos, tutorials, and other other awesome content!

  • Total Time: 112 Hours 

  • Overall: Well kept journal! Reasonably detailed and excellently formatted!

• Conclusion: Ronald has made many great contributions to the wiki and has kept a detailed journal! Excellent job!