diego1401

diego1401 / GaussianWrapping

Public

Official implementation of "From Blobs to Spokes: High-Fidelity Surface Reconstruction via Oriented Gaussians"

87
5
100% credibility
Found Apr 10, 2026 at 87 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
C++
AI Summary

Gaussian Wrapping reconstructs detailed, textured 3D surface meshes from multi-view images using oriented 3D Gaussians, excelling at thin structures.

How It Works

1
🔍 Discover Gaussian Wrapping

You find this tool online and get excited about turning your photos into stunning 3D models of real objects, even skinny details like bike spokes.

2
📸 Gather your photos

Collect a set of pictures of your object from different angles, like circling around it with your phone.

3
⚙️ Set up the tool

Follow simple steps to prepare your computer so it can learn from your images.

4
🚀 Start training

Feed your photos to the tool and let it build a 3D understanding of your scene.

5
✨ Watch magic happen

Your assistant analyzes the images and creates a smart 3D point cloud that captures every detail beautifully.

6
🧩 Extract the surface

Pull out a smooth, watertight 3D mesh ready for viewing or printing.

7
🎨 Add lifelike textures

Paint realistic colors and details onto your model from the original photos.

✅ Enjoy your creation

You now have a high-quality 3D model perfect for sharing, printing, or exploring in any viewer.

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Star Growth

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AI-Generated Review

What is GaussianWrapping?

GaussianWrapping is the official C++ implementation of a technique that reconstructs watertight, textured surface meshes from multi-view images via oriented 3D Gaussians. It processes COLMAP datasets to produce compact meshes capturing fine details like bicycle spokes, solving the "blobs" problem in Gaussian Splatting outputs. Run end-to-end scripts for training, extraction, and texturing, with options for RaDeGS or median-depth rasterizers.

Why is it gaining traction?

It delivers meshes at a fraction of competitors' size while topping metrics on Tanks and Temples and MipNeRF360 benchmarks. Primal adaptive meshing refines inputs via gradient descent on Gaussian occupancy, and a Blender add-on enables precise bounding volumes for object extraction. Developers grab it via official GitHub releases for quick high-fidelity results without manual tuning.

Who should use this?

3D reconstruction engineers processing SfM data for AR/VR assets, robotics sims needing thin structures, or VFX artists texturing props from photo captures. Ideal for Gaussian Splatting users wanting deployable meshes over raw point clouds.

Verdict

Solid pick for Gaussian-to-mesh pipelines—try the official GitHub CLI scripts if you're in that stack. 1.0% credibility and 87 stars signal early maturity; docs are README-focused with benchmarks, so test on your data before production.

(198 words)

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