Building physical AI systems, such as humanoid robots that work in assembly lines or autonomous vehicles that navigate busy streets, requires millions of hours of testing in high-fidelity simulations before deployment. Simulation and synthetic data generation technologies like NVIDIA Omniverse and NVIDIA Cosmos have made this economically viable, compressing years of real-world testing into weeks of virtual iteration.

But a bigger challenge remains: over 50% of these simulations end up unusable because of bad input data – slowing teams and wasting millions in compute costs.

Today, we're announcing FiftyOne Physical AI Workbench—a standardized data engine that provides turnkey access to NVIDIA neural reconstruction and generative AI models. Integrated with Omniverse and Cosmos, the Physical AI Workbench transforms raw multimodal sensor data processing into high-fidelity simulation-ready datasets. Teams can then create, test, and augment physical AI systems with unprecedented speed and realism.

Why Physical AI data challenges are different

Developing reliable physical AI systems requires teaching machines to understand and act within the physical world, a challenge far more complex than anything faced in traditional AI domains. These systems process petabytes of multimodal sensor data, which includes inputs from multiple sources: camera feeds, LiDAR point clouds, radar, IMU sensors, and geolocation. We're talking about 1000x more data than typical large language model (LLM) applications.

The challenge isn't just volume. In fact, it’s that all these sensors must work in perfect harmony for downstream high-fidelity simulations of the physical world. For example, if an autonomous vehicle’s camera captures a pedestrian at timestamp 10:23:45.127, the LiDAR should capture the same pedestrian at the exact same moment. If they're off by even a few milliseconds, or if the spatial calibration between them drifts by a few degrees, downstream 3D reconstruction places that pedestrian in the wrong location. This means that not only are the neural reconstructions and simulations inaccurate, but simulations will train models incorrectly. These models deployed in the real world can misjudge distances and object motion, resulting in unsafe or incorrect decisions.

Good neural reconstruction

Poor-quality neural reconstruction

NVIDIA Omniverse NuRec neural reconstruction libraries and open Cosmos world foundation model platform render, augment, and simulate the physical world with incredible fidelity. However, when simulations run on corrupted data, they waste millions of compute dollars on unusable results. These flawed neural reconstructions also can’t generate reliable synthetic data for edge cases.

The FiftyOne Physical AI Workbench ensures that data feeding into this simulation workflow meets the data quality standards upfront, and gives engineers direct access to NVIDIA’s physical AI libraries for building high-quality neural reconstructions and synthetic data generation.

The foundation: Voxel51 meets NVIDIA Physical AI

Built by Voxel51 and powered by NVIDIA technologies, the FiftyOne Physical AI Workbench turns real-world sensor data into clean, accurate digital scenes that are used to train, test, and refine AI models. The goal is to make real and simulated data interoperable, reproducible, and actionable at scale.

At the core of this workbench is Voxel51’s data-centric AI platform that integrates with 3D reconstruction and synthetic data generation tools.

Read the solution brief

Voxel51 is the multimodal AI data engine for data curation, visualization, annotation, and model evaluation for building high-quality datasets and models. The engine provides capabilities to audit, enrich, and generate synthetic data and high-fidelity neural reconstruction.

NVIDIA technologies make Physical AI simulation possible by providing a solid foundation for rendering real-time 3D reconstructions and photorealistic scene variations:

NVIDIA Cosmos Dataset Search is a GPU-accelerated vector search workflow that quickly embeds and searches video datasets. It enables efficient search, retrieval, and understanding of real-world visual data.
NVIDIA Omniverse NuRec (Neural Reconstruction) is a set of technologies for neural reconstruction and rendering. It enables developers to use their existing fleet data to reconstruct high-fidelity digital twins, simulate new events, and render sensor data from novel points of view.
NVIDIA Cosmos is a set of world foundation models that generate world states and amplify data variations. Cosmos Transfer – a style transfer model takes your reconstructed scene and applies realistic variations such as changing weather, lighting, or time of day while preserving scene structure and sensor geometry.

Together, these technologies create a powerful Physical AI data pipeline: ingest data → validate and enrich it → perform efficient search and scene understanding with embeddings using NVIDIA Cosmos Dataset Search → reconstruct with NVIDIA Omniverse NuRec → create synthetic scene variations with NVIDIA Cosmo Transfer for scalable simulation.

Why neural reconstruction is key to accelerating high-fidelity simulations

Neural reconstruction bridges real-world data and virtual testing. It rebuilds the physical world in 3D so AI systems can accurately learn and make decisions before they face reality. Neural reconstruction lets teams take hours of sensor data (camera or LiDAR) from a single autonomous vehicle test drive and transform it into a complete digital twin. Users can replay real-world scenarios from any angle, re-render them under new lighting or weather conditions, and simulate different actors or trajectories without returning to the test site for data collection.

Accurate reconstructions enable the creation of high-fidelity simulations that mirror the real world with precision, so every object and condition behaves as it would in reality.

How FiftyOne Physical AI Workbench functions

FiftyOne Physical AI Workbench sits at the beginning of the high-fidelity simulation pipeline. It ensures every reconstruction and simulation starts with trustworthy data.

The workbench delivers three core capabilities.

1. Audit and validate: Catch and fix errors before they cost you

The workbench automatically audits Physical AI inputs across 75+ critical checkpoints:

Sensor calibration verification (camera intrinsics and extrinsics)
Temporal synchronization (all sensors time-aligned)
LiDAR-to-camera projection accuracy
Coordinate system consistency
Metadata completeness and formatting

You get validated datasets that are ready for enrichment and neural reconstruction. No more discovering calibration errors weeks into your simulation data pipeline. The audit phase also produces human-readable QA reports that pinpoint exactly what's wrong and where.

2. Enrich: Add structure and context with AI data enrichment

Once validated, the workbench transforms raw sensor streams into structured, queryable datasets:

Auto-labeling: Automatically generate labels for objects, lanes, and scene elements
Scene understanding: Extract semantic information using visual Q&A
Visual 3D inspection: View LiDAR point clouds overlaid on camera images
Embeddings and search: Find similar scenarios with Cosmos Dataset Search
Metadata enrichment: Add searchable attributes to curate specific edge cases

This phase is where teams move from "we have sensor data" to "we have structured, searchable datasets."

3. Generate synthetic data: Reconstruct and augment at scale

The final stage prepares datasets for reconstruction and synthetic data generation:

Format normalization: Convert data into standardized formats compatible with neural reconstruction tools
Trigger reconstructions: Direct integration with NVIDIA Omniverse NuRec for 3D reconstructions
Synthetic data variation: Generate scene variations (weather, lighting, time of day) with NVIDIA Cosmos Transfer
Quality verification: Inspect reconstructions directly within the workbench

The three stages form a data flywheel: validated datasets power more reconstructions, generate synthetic variations, and model improvement. Every step is traceable. Every decision is auditable. And most importantly, every simulation starts with data you can trust.

What this means for Physical AI teams

By combining NVIDIA’s simulation and reconstruction capabilities with Voxel51’s expertise in data-centric AI, FiftyOne Physical AI Workbench gives development teams a standardized workflow to connect real and synthetic data and validate models in ways they couldn’t before. Teams can reduce costly real-world data collection and expand testing coverage for rare or safety-critical events – all while maintaining full auditability and organizational transparency.

Reduce wasted compute: Catch data quality issues before investing in expensive simulations. A single prevented failure saves hours or days of expensive GPU costs.

Prevent downstream failures: Train and validate every model on trustworthy data, minimizing the risk of silent performance regressions and costly rework.

Increase simulation ROI: Compress weeks of debugging into hours of automated validation. When every simulation uses high-quality input data, you get usable results.

Scale confidently: Process petabytes of sensor data with consistent quality standards, enabling quicker prototype to production.

Get started

As physical AI transitions from research labs to production, the industry needs a reliable end-to-end tooling infrastructure that scales with complexity. The FiftyOne Physical AI Workbench delivers that infrastructure, bringing the best-in-class NVIDIA foundation models and simulation tools directly to your workflow.

FiftyOne Physical AI Workbench is available now for enterprise customers.

Get in touch with the Physical AI experts to see how you can streamline your raw data into accurate simulation-ready datasets.

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