Dr. Zheng (Thomas) Tang

Dr. Zheng (Thomas) Tang

Senior Deep Learning Engineer at NVIDIA, expert in computer vision and machine learning

Releasing the Physical AI Smart Spaces Dataset for AI City Challenge 2024 & 2025

1 minute read

Published:

I’m excited to share that our team at NVIDIA has officially released the Physical AI Smart Spaces dataset as part of the Open Physical AI Dataset initiative, announced during GTC 2025. This dataset supports Tracks 1 of the AI City Challenge 2024 & 2025, helping researchers push the boundaries of real-time, multi-camera spatial AI.

As a lead contributor, I helped drive the creation and integration of this large-scale synthetic dataset—designed specifically for multi-target multi-camera (MTMC) tracking, 3D occupancy prediction, and smart infrastructure simulation. It is one of the most comprehensive synthetic datasets ever released for spatial AI, capturing warehouse, factory, and public space scenarios with fine-grained annotations and synchronized multi-view video.

Built with NVIDIA Omniverse and aligned with our Metropolis AI workflows, the dataset enables the development of robust computer vision systems for real-world deployment. From training deep learning models to validating digital twin applications, Physical AI Smart Spaces serves as a benchmark-ready resource for cutting-edge research and development.

We’re proud to empower the global research community with open tools and high-quality data. Whether you’re developing advanced trackers, occupancy estimators, or large-scale analytics systems, this dataset is built to accelerate your innovation.

Explore the dataset here:
👉 huggingface.co/datasets/nvidia/PhysicalAI-SmartSpaces

 NVIDIA’s Open Physical AI Dataset

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