Project 1 Proposal : LINK

Learning Goals

2 | 5 | Goal 1: articulate AR/VR visualization software tool goals, requirements, and capabilities; 

1 | 4 | Goal 2: construct meaningful evaluation strategies for software libraries, frameworks, and applications; strategies include surveys, interviews, comparative use, case studies, and web research; 

1 | 4 | Goal 3: execute tool evaluation strategies;

2 | 4 | Goal 4: build visualization software packages; 

1 | 4 | Goal 5: comparatively analyze software tools based on evaluation; 

2 | 5 | Goal 6: be familiar with a number of AR/VR software tools and hardware; 

3 | 5 | Goal 7: think critically about software; 

3 | 5 | Goal 8: communicate ideas more clearly; 

1 | 5 | Goal 9: Contribute high-quality, lasting content to the VR Software Wiki to aid future researchers and students.

Project Ideas: 

1. Visualizing AI Embeddings

I want to get a high-dimensional dataset like Word2Vec, preprocess it for 3D projection, and implement a VR point-cloud renderer that supports selection. I plan to test some kind of sensory navigation interactions to see if physical movement aids understanding. For a class activity, people can perform an A/B cluster search to evaluate different visualization metaphors. My deliverables will include a comparative wiki entry on density vs. legibility in 3D plots and a reusable Unity/WebXR component for loading CSV embedding data.

2. VR Movement Data Visualization

I want to get skeletal movement data for correct and incorrect squat forms and develop an overlay system in VR to superimpose the ideal form over the user's path. You can use visual deviation indicators to highlight unnatural movements. In a activity, people will use the tool to guide a peer, analyzing the efficacy of the visual cues. Deliverables will be a case study on immersive spatial feedback versus video playback and a wiki tutorial on importing BVH motion files.

3. Immersive Network Traffic Visualization

My goal is to capture network traffic data to build a VR visualization where devices are nodes and data packets are animated edges representing bandwidth. I will create filtering tools to isolate connections by grabbing nodes. The class will participate in a heuristic evaluation to identify a simulated security breach and rate the tool's usability. Potential deliverables include a table evaluating 3D graph layout libraries for VR performance and a wiki page on design patterns for selecting nodes in dense graphs.

Project Plan:

Goal: Build a VR point-cloud visualization system for exploring high-dimensional AI embeddings (Word2Vec), with interactive selection and navigation, culminating in a comparative study of density vs. legibility in 3D plots.

Platforms: Unity (Quest) and/or WebXR (A-Frame/Three.js)

Deliverables: Reusable Unity/WebXR component for loading CSV embedding data. Comparative wiki entry on density vs. legibility in 3D point-cloud plots. In-class A/B cluster search activity with evaluation data

Milestone Schedule

Week 1: Data Acquisition & Preprocessing Pipeline

Monday 2/10 — Dataset Selection & Initial Preprocessing

• Download pre-trained Word2Vec embeddings 

• Write Python script to extract a meaningful subset (e.g., 1,000–5,000 words from a semantic category like emotions, animals, or places)

• Apply dimensionality reduction (PCA or t-SNE)

• Export results to CSV format 

• Deliverable: CSV file with 3D-projected embeddings ready for visualization

Wednesday 2/12 — Visualization Framework Setup

• Set up Unity project with XR Interaction Toolkit (experiment w/ WebXR development environment)

• Review wiki resources: IATK Basics, DinoVR for Point Cloud Data

• CSV parser to load data

• Render initial static point cloud (no interaction yet)

• Deliverable: Basic point cloud rendering from CSV data

Week 2: Core Interaction Development

Wednesday 2/19 — Selection & Labeling System

• Implement point selection via ray-casting (controller or gaze-based)

• Display word labels on hover/selection (floating text or tooltip UI)

• Add color coding by cluster or semantic category

• Implement basic camera/locomotion controls (teleportation or smooth movement)

• Deliverable: Interactive point cloud with selection and labels working

Week 3: Navigation & Density Experiments

Monday 2/24 — Sensory Navigation Prototype

• Implement physical movement mapping (room-scale walking corresponds to data navigation)

• Add scaling controls (zoom in/out of point cloud)

• Create two visualization modes for A/B testing:

  • Mode A (Dense): All points visible, smaller point size, full dataset

  • Mode B (Legible): Filtered points, larger labels, decluttered view with LOD (level of detail)

• Deliverable: Two toggleable visualization modes ready for comparison

Wednesday 2/26 — Refinement & Activity Design

• Polish interactions and fix bugs from testing

• Design in-class activity protocol:

  • Task: "Find all words related to [category X] as quickly as possible"

  • Metrics: Time to completion, accuracy, subjective preference

  • Create simple data collection form (Google Form or in-app logging)

• Prepare activity instructions and evaluation questionnaire

• Deliverable: Activity protocol document and data collection system

Week 4: In-Class Activity & Analysis

Monday 3/03 — Final Pre-Activity Testing

• Deliverable: Fully tested build ready for class activity

Wednesday 3/05 — Begin Wiki Documentation

• Deliverable: Wiki entry draft (technical sections complete)

Week 5: In-Class Activity 

Activity: A/B Cluster Search Evaluation

Participants split into two groups; each starts with a different visualization mode. Find and select all words related to 'weather' (or similar category) within the point cloud. Group A starts with Dense view, Group B starts with Legible view; then swap. Completion time, accuracy (% correct selections), NASA-TLX workload rating, preference ranking

Week 5–6: Analysis & Final Deliverables

By 3/12–3/13:

• Analyze activity data (quantitative + qualitative)

• Complete wiki entry with findings and recommendations

HOURS journal

1/26/26 - 4 Hours

• Joined up slack and introduced myself

• Read through the wiki and researched new VR tech (ProtienVR, Gaussian Splatting)

• Added my proposed changes to my Journal

• Explored Kenny Gruchalla's bio and research and added my questions to Gdoc. 

1/29/26 - 10 Hours

• Worked on project ideas

• Completed Google earth setup and activity.

• Completed Dino VR Setup

• Completed Virtual Desktop Setup

• Completed ShapesVR Lab

2/4/26 - 4 Hours

Completed In class DinoVR assignment 

Drafted and added project proposal and activites to journal 

Researched mapping mapping points using Unity/WebXR 

2/8/26 - 5 Hours

Completed project proposal  

Completed project proposal powerpoint presentation

Researched tools needed for project (Unity, Python, etc)