Reconstructing the geometry and appearance of a given scene is a fundamental task in 3D computer graphics and computer vision. Recently, radiance fields have emerged as a representation of light transport in the scene, allowing, as a byproduct, also to extract 3D geometry solely from multi-view imagery. Initially designed for RGB captures, existing approaches have been extended to other sensor modalities. Among these, transient imaging — measuring the time-of-flight of light at picosecond resolution — has emerged as a promising alternative, offering rich spatio-temporal information to improve reconstruction quality from limited viewpoints and obstructed views. However, its applicability to outdoor scenarios has been highly problematic due to interference from ambient light and the different sensor behavior under high-photon-flux conditions typical of outdoor settings. Addressing this gap, we introduce Transient LASSO, a neural scene reconstruction method operating on raw transient measures of outdoor in-the-wild captures to accurately reconstruct the underlying scene geometry and properties. We demonstrate the effectiveness of our method across a variety of outdoor environments, including complex urban scenes with dense traffic and infrastructure. Finally, we also show the potential use cases of our method for downstream applications such as sensor parameter optimization.