Sponsor: Prof. Raffaele De Amicis
Course: CS 461–463 (Capstone 2025–2026)
Institution: Oregon State University
Organization: XRDrone Team
XRDrone is a real-time drone-to-VR streaming and object recognition system. The project aims to build a Unity-based application (target: Android/Meta Quest 2) that receives live video from a DJI Neo drone, renders it in immersive 3D space, and performs on-device object detection to overlay visual cues such as bounding boxes, labels, or shader-based highlights.
- Drone Video to Quest 2:
Stream ≥ 720p video at ≥ 24 FPS with ≤ 300 ms glass-to-glass latency, rendered on a hemispherical surface to reduce distortion. - On-Device Vision:
Detect at least two object classes (e.g., “tree,” “person,” “vehicle”) in real time (≥ 15 FPS) using optimized ML models. - VR User Experience:
Provide a cockpit-style HUD showing stream status, detection confidence, and toggles for overlays or recentering. - Engineering Deliverables:
Document architecture, streaming protocol rationale, model choice, benchmarking results, and safety/ethical notes.
| Category | Target Metric |
|---|---|
| Streaming Reliability | ≥ 24 FPS, < 2 % dropped frames, recover from forced disconnect |
| Latency | ≤ 300 ms median glass-to-glass |
| Vision Accuracy & Speed | ≥ 0.5 F1 on labeled dataset (≥ 150 frames), ≥ 15 FPS inference |
| VR UX & Effects | Readable overlays, unobtrusive HUD, ≥ 1 shader/VFX linked to detections |
| Engineering Quality | Clear architecture, reproducible build, configuration, and logging |
| Name | Bio |
|---|---|
| William Brennan | Interested in VR and has prior experience coding in Unreal Engine. |
| Troy Diaz | Worked on a machine learning project with large image datasets and aims to apply that experience to XRDrone’s real-time detection. |
| Balakrishna Thirumavalavan | Drawn to the project’s focus on VR display overlays and intrigued by its real-time detection functionality. |
| Guillermo Morales | Interested in AR/VR and machine learning for human–computer interaction. |
- Primary Channel: Microsoft Teams (OSU Capstone workspace)
- Team Meetings: Fridays 10–11 AM in Person
- TA Meetings: Fridays 11 AM on Zoom
- Sponsor Meetings: Time TBD with Prof. Raffaele De Amicis
- Response-Time Expectation: Pull requests and messages are reviewed within 24 hours
- Stand-ups: Mondays and Thursdays – 10-minute check-ins on Teams
- Default branch:
main - Branch flow:
feature/* → pull request → ≥1 review → merge - Review cadence: All PRs reviewed within 24 hours via Teams notification.
- PR template:
.github/PULL_REQUEST_TEMPLATE.mdcreated to guide contributions.
- TBD
- TBD
This project is licensed under the MIT License – see the LICENSE file for details.
Special thanks to Prof. Raffaele De Amicis for project sponsorship and guidance,
and to Oregon State University’s School of EECS for supporting the XRDrone Capstone.