SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering
We propose a method to allow precise and extremely fast mesh extraction from 3D Gaussian Splatting. Gaussian Splatting has recently become very popular as it yields realistic rendering while being significantly faster to train than NeRFs. It is however challenging to extract a mesh from the millions of tiny 3D gaussians as these gaussians tend to be unorganized after optimization and no method has been proposed so far. Our first key contribution is a regularization term that encourages the gaussians to align well with the surface of the scene. We then introduce a method that exploits this alignment to extract a mesh from the Gaussians using Poisson reconstruction, which is fast, scalable, and preserves details, in contrast to the Marching Cubes algorithm usually applied to extract meshes from Neural SDFs. Finally, we introduce an optional refinement strategy that binds gaussians to the surface of the mesh, and jointly optimizes these Gaussians and the mesh through Gaussian splatting rendering. This enables easy editing, sculpting, rigging, animating, compositing and relighting of the Gaussians using traditional softwares by manipulating the mesh instead of the gaussians themselves. Retrieving such an editable mesh for realistic rendering is done within minutes with our method, compared to hours with the state-of-the-art methods on neural SDFs, while providing a better rendering quality.
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Computer vision researchers developed a way to create detailed 3D models from images in just minutes on a single GPU. Their method, called SuGaR, works by optimizing millions of tiny particles to match images of a scene. The key innovation is getting the particles to align to surfaces so they can be easily turned into a mesh.
Traditionally 3D modeling is slow and resource heavy. Laser scans are unwieldy. Photogrammetry point clouds lack detail. And neural radiance fields like NeRF produce amazing renders but optimizing them into meshes takes hours or days even with beefy hardware.
The demand for easier 3D content creation keeps growing for VR/AR, games, education, etc. But most techniques have big speed, quality, or cost limitations holding them back from mainstream use.
This new SuGaR technique combines recent advances in neural scene representations and computational geometry to push forward state-of-the-art in accessible 3D reconstruction.
It starts by leveraging a method called Gaussian Splatting that basically uses tons of tiny particles to replicate a scene. Getting the particles placed and configured only takes minutes. The catch is they don't naturally form a coherent mesh.
SuGaR contributes a new initialization and training approach that aligns the particles with scene surfaces while keeping detail intact. This conditioning allows the particle cloud to be treated directly as a point cloud.
They then apply a computational technique called Poisson Surface Reconstruction to directly build a mesh between the structured particles in a parallelized fashion. Handling millions of particles at once yields high fidelity at low latency.
By moving the heavy lifting to the front-end point cloud structuring stage, SuGaR makes final mesh generation extremely efficient compared to other state-of-the-art neural/hybrid approaches.
Experiments showed SuGaR can build detailed meshes faster than previous published techniques by orders of magnitude, while achieving competitive visual quality. The paper shares some promising examples of complex scenes reconstructed in under 10 minutes.
There are still questions around handling more diverse scene types. But in terms of bringing high-quality 3D reconstruction closer to interactive speeds using accessible hardware, this looks like compelling progress.
TLDR: Aligning particles from Gaussian Splatting lets you turn them into detailed meshes. Makes high-quality 3D better, faster, cheaper.
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