asset-harvester

Asset Harvester | System Model Card Paper** | **Live Demo!** | **Project Page** | **Code** | **Model** | **Data** **Description:** Asset Harvester** is an image-to-3D model and end-to-end system that converts sparse, in-the-wild object observations from real driving logs into complete, simulation-ready assets. The model generates 3D assets from a single image or multiple images of vehicles, VRUs or other road objects extracted from autonomous driving sessions. To run Asset Harvester, please check our **codebase**. Support 📣 **Asset Harvester usage questions**: post on the NVIDIA Developer Forum (Omniverse / NuRec). 🐛 **Asset Harvester code bugs / documentation issues / feature requests**: file a GitHub issue using the appropriate template (Bug report, Documentation request, or Feature request) at https://github.com/NVIDIA/asset-harvester/issues/new/choose . The relevant NVIDIA responder is auto-assigned. 🚨 **Security vulnerabilities**: please use NVIDIA's Vulnerability Disclosure Program. Do not file public issues. (Note: the Hugging Face Community tab for the Asset Harvester model will be disabled on 08/04/2026. Please use the channels above.)* Asset Harvester** turns real-world driving logs into complete, simulation-ready 3D assets — from just one or a few in-the-wild object views. It handles vehicles, pedestrians, riders, and other road objects, even under heavy occlusion, noisy calibration, and extreme viewpoint bias. A multiview diffusion model generates consistent novel viewpoints, and a feed-forward Gaussian reconstructor lifts them to full 3D in seconds. The result: high-fidelity 3D Gaussian splat assets ready for insertion into simulation environments. The pipeline plugs directly into NVIDIA NCore and NuRec for scalable data ingestion and closed-loop simulation. Here's how the model checkpoints in this repo are used in the end-to-end system following the order in the pipeline: The AV object Mask2former instance segmentation model is used for image processin