Gaussian Splatting Supervision

Train 3D Gaussian Splatting representations from multi-view images or video, optimizing the position, covariance, color, and opacity of Gaussian primitives to reconstruct a 3D scene. 3D Gaussian Splatting represents a scene as a collection of anisotropic 3D Gaussians, each defined by a position, covariance matrix, color (with spherical harmonics for view-dependence), and opacity. Training optimizes these parameters through differentiable rendering: Gaussians are projected to the image plane, rasterized, and compared to training views using photometric loss. Adaptive density control splits and clones Gaussians where reconstruction error is high, and prunes near-transparent ones.