Asset Harvester is an image-to-3D model and end-to-end pipeline that converts sparse, in-the-wild object observations from autonomous driving logs into complete, simulation-ready 3D assets. It combines large-scale curation of object-centric training tuples, geometry-aware preprocessing across heterogeneous sensors, and sparse-view-conditioned multiview generation with 3D Gaussian lifting. The resulting assets support object-level manipulation and large-viewpoint novel-view synthesis for closed-loop autonomous-driving simulation.