Voxel51 Data Generation
Close data gaps and build simulation-ready datasets—without new collection runs. Voxel51 provides the tools to audit, enrich, and prepare data for generating high-fidelity 3D reconstructions and synthetic scenes.
Quality reconstructions
Build neural reconstructions on a foundation of high-quality data
Errors such as sensor misalignment and calibration drift silently corrupt 3D reconstructions. Make every compute resource count by validating your multi-sensor data before it reaches simulation.
Good neural reconstruction of vehicles on a road
Poor quality neural reconstruction of vehicles on a road.
Validate and Enrich
Create structured and validated perception datasets
Automatically audit and enrich real-world sensor data to generate synthetic scene variations and high-fidelity digital twins.
Audit multimodal input streams
Automatically audit pose calibrations, sensor misalignments, coordinate conventions, and metadata consistency.
Enrich data by adding context
Enhance unstructured datasets with auto-labels, scene understanding, image/video search, and metadata.
Integrate
Bridge the gap between real-world sensor data and synthetic simulation
Dive deeper into how Voxel51 integrates with NVIDIA Omniverse™ NuRec libraries and NVIDIA Cosmos™ to power the creation of rich, reconstructable scenes and variations.
Workflow
Democratize your data
Generate scalable data pipelines for your entire organization: from data audit and enrichment to photorealistic digital twins.
Catch input sensor issues
Audit dashboard
View data distribution
Generate depth maps
Auto-label data
Video search and retrieval
Automatic QA
Automatically flag input data inconsistencies and generate audit-ready reports.
Prevent downstream failures
Identify data reconstruction gaps early to ensure model training is based on reliable data.
Increase simulation ROI
Speed up development and save costs by eliminating fragmented workflows and rework.
“Customers tell us that over 50% of Physical AI simulations are unusable due to poor quality data. Teams are burning millions on compute only to realize that their simulation results are unreliable."
Brian Moore
CEO and Co-founder, Voxel51
Questions?
We have answers.
Data generation for Physical AI is the process of creating, augmenting, and enriching datasets that train and validate AI systems like autonomous vehicles and robots. Because collecting real-world data at scale is expensive and time-consuming, teams use techniques like 3D reconstruction, synthetic scene generation, and data augmentation to close gaps and build more diverse datasets.
Data augmentation takes existing real-world data and applies realistic transformations like changing weather, lighting, or time of day, to increase dataset diversity without new collection runs. Synthetic data generation goes further, creating entirely new scenes from simulation for training on rare or edge case scenarios. Voxel51 supports both, integrating with world foundation models like NVIDIA Cosmos to generate scene variations and high-fidelity digital twins.
No. Voxel51 is designed to make data generation accessible to ML engineers and data scientists without requiring deep simulation expertise. For teams that need additional support, our experts work directly with you to generate high-fidelity 3D reconstructions using NVIDIA Omniverse NuRec.
The sim-to-real gap is the performance drop AI models experience when moving from simulated training environments to real-world deployment, primarily caused by differences in visual appearance, sensor noise, and environmental conditions. Synthetic data generation helps close this gap by creating photorealistic training data that more closely mirrors real-world conditions, reducing the amount of real-world data needed. Voxel51 integrates with world foundation models to generate scene variations that bridge the gap between simulation and reality.
Yes. While Voxel51 offers deep integration with NVIDIA's suite of tools, it’s built on a flexible foundation, allowing you to work with any reconstruction and synthetic data generation tools.
Voxel51 supports a wide range of multimodal data, including images, video, LiDAR, radar, depth maps, and segmentation masks, ensuring consistency across all sensor streams before data enters the reconstruction or simulation pipeline.
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