Robust 3D edge reconstruction from in-the-wild data remains a key challenge in robotics and SLAM, where scenes are often noisy, unstructured, and captured under imprecise conditions. Recent methods based on neural implicit representations offer compact reconstructions but suffer from high computational costs and poor localization of fine edge detail due to reliance on NeRF-style volumetric rendering. While Gaussian Splatting offers a fast and accurate alternative, its use in edge extraction has been limited to clean or synthetic datasets with precise initialization.

We present EdgeSplats, a method for 3D edge reconstruction that operates effectively on real-world data without requiring clean geometric priors. Our approach leverages the 3DGS-MCMC pipeline to train edge-aligned Gaussians directly from posed images. A graph-based clustering stage then extracts edge-consistent splats by exploiting spatial and directional coherence.

We demonstrate that EdgeSplats improves both reconstruction quality and training speed over existing methods, demonstrating strong performance on noisy, real-world inputs.