Gaussian Splatting technology represents a remarkable advancement in 3D scene capture. Unlike traditional methods like photogrammetry, which generates meshes and textures, this technique models the scene with millions of small volumetric elements. This inherently allows for the reproduction of complex optical effects, resulting in extremely realistic visualizations. We explore its operation and its potential integration into workflows like Blender.
From Point Cloud to Differentiable Representation 🔬
The process begins with a point cloud obtained from LIDAR sensors or cameras. Each point is transformed into a 3D Gaussian, an ellipsoid with attributes of color, opacity, and rotation. The key lies in differentiable rasterization, which allows training these parameters so that, when projected into 2D, they faithfully reconstruct the source images. This captures light transport, including semi-transparencies and specular reflections, without the need to model them manually.
Goodbye to Cleaning Meshes, Hello to Cleaning Splats 😅
It seems we've traded one problem for another. Before, we spent hours removing photogrammetry artifacts and retopologizing. Now, our new pastime will be dealing with ghost splats and adjusting the density of stray ellipsoids. The promise is to never touch a smoothing modifier again, though we might miss the simplicity of a good triangle. Progress, sometimes, just changes the type of headache.