Project Info
Inspiration
We were inspired by the increasing demand for accessible 3D modeling tools and the incredible potential of augmented reality. Seeing how AR could guide users to capture high-quality photos, we envisioned a tool that bridges real-world imagery and accurate 3D reconstructions.
What it does
SnapAR3D uses AR to guide you in capturing optimal photos of an object, while precisely recording the camera’s positions and orientations relative to that object. This data is transformed into matrices and imported into NVIDIA Diffractive Reconstruction (nvdifrec) to generate high-quality 3D models.
How we built it
We built SnapAR3D by integrating ARKit to capture photos and sensor data, and COLMAP to format the camera parameters. A custom conversion pipeline transforms the captured data into NeRF-compatible transformation matrices, which nvdifrec uses for reconstruction. We also developed a lightweight web host for easy sharing of the resulting transforms.json file.
Challenges we ran into
One of our biggest challenges was fine tuning—adjusting the photo capture process and data transformation parameters to ensure that the final 3D model was as accurate and detailed as possible. We also had to address coordinate system discrepancies between ARKit and NeRF, and seamlessly integrate data from multiple sources.
Accomplishments we're proud of
Successfully guiding users with AR to capture optimal images. Training a model that is comparable in quality and much faster in training time
What we learned
We deepened our understanding in AR app development, gained valuable insights into how 3D models are built using 2D photos, and learned to always push through the challenges because it will be worth it in the end.
What's next
Next, we plan to improve the user interface for an even smoother capture experience, expand support for diverse objects and scenes, and fine tune the model further while enhancing real-time feedback during data capture.
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Analysis
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Technology
- PythonIn code
- JavaScriptClaimed
- SwiftClaimed
1 of 3 appear in the indexed code. 2 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.
AI coding agents
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Codebase size
Source size
29 KB
Source files
10
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
wwangg22/treehcks
12 files · 30 KB · @ 5d0c210
Structure
Application logic
10 files · 83%Domain rules, services and shared utilities.
Supporting
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Languages
- Python100%
Share of indexed source by file size. Binary and vendored files are excluded.
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