Since day one we've focused on one problem: generating synthetic data that perception models can actually learn from. A physics-based simulation core, broad scenario coverage, and generation at scale for deploying trustworthy physical AI.
01 Physics-based simulation
Perception models don't see the world. They see what the sensor sees.
That's why Anyverse simulates the entire physical chain, from light source to sensor output. Our proprietary render engine computes the spectral radiance of every light beam as it interacts with materials, optics, and sensor electronics. The result is data that is physically correct, not just realistic — matching the radiometric behavior of your hardware and minimizing the sim-to-real gap.
Hyperspectral render engine
Pure spectral path tracing calculates per-wavelength radiance instead of approximating RGB values. Lights, materials, and atmospheric conditions are modeled with physical realism.
Sensor-pipeline modeling & augmentation
We simulate the sensor itself — optics, spectral response, exposure, noise, ISP. Changing those physical parameters generates genuinely new observations of the scene, as a real camera would capture them — not manipulated pixels, and with ground truth preserved.
One scene, any sensor configuration
Scene and sensor models are independent. Re-generate a scene with a different sensor, added parameters, or a full multi-sensor rig, with the consistent geometry and timing that sensor-fusion work requires.
Because the engine works at the spectral level, supporting a new modality doesn't mean a new renderer — it means a new sensor model on the same physical foundation. These are the modalities Anyverse simulates today.
Spectral simulation of the imaging chain, from photons to pixels.
Visible-spectrum camera
RAW & ISP · 0.4–0.7 µm
NIR — Near-Infrared
0.7–1 µm
SWIR — Short-Wave Infrared
1–3 µm
MWIR — Mid-Wave Infrared
3–5 µm
LWIR — Long-Wave Infrared
8–14 µm
ToF — Time-of-Flight
Active depth imaging
Active ranging — LiDAR & Radar
Signal-level simulation of emitted energy and its return.
LiDAR — Spinning
Mechanical 360°
LiDAR — Solid-state
MEMS / flash / OPA patterns
Radar — FMCW / mm-wave
76–81 GHz automotive band
Radar — UWB
Ultra-wideband
Radar — PCR
Pulsed coherent radar
Your sensor here
Custom sensor models
02 Scenario coverage
Coverage you can measure, not hope for.
Physical accuracy is only half the problem — a dataset must also cover the operational domain it validates. Anyverse structures that coverage through domain-specific scenario libraries: curated, parameterized scenarios for each use case, sampled across every condition your system must handle. You define the distribution up front, not after collection.
CURATED LIBRARIES, EXPANDED INTO MEASURED COVERAGE
Scenario libraries per use case
PROCEDURALLY GENERATED — YOU SET THE DISTRIBUTION
lightingdemographicsweatherocclusion+
AUTO-ANNOTATED BY CONSTRUCTION
Measured coverage of the operational domain
Distribution-based domain randomization
Variability is sampled from distributions you control. Ensure uniform coverage across demographics, lighting, weather, and object configurations, or deliberately oversample the edge cases your model struggles with.
Ground truth as a byproduct of simulation
Because every scene is fully known, annotation is pixel-perfect by construction: 2D/3D boxes, semantic and instance segmentation, depth, normals, motion vectors, materials, and spectral channels — with zero labeling cost or human error.
Safety-critical scenarios on demand
Generate the dangerous, rare, or hard-to-capture scenarios that real-world collection can't safely or practically reach — as often as you need them.
Distribution-based domain randomization
Variability is sampled from distributions you control. Ensure uniform coverage across demographics, lighting, weather, and object configurations, or deliberately oversample the edge cases your model struggles with.
Ground truth as a byproduct of simulation
Because every scene is fully known, annotation is pixel-perfect by construction: 2D/3D boxes, semantic and instance segmentation, depth, normals, motion vectors, materials, and spectral channels — with zero labeling cost or human error.
Safety-critical scenarios on demand
Generate the dangerous, rare, or hard-to-capture scenarios that real-world collection can't safely or practically reach — as often as you need them.
CURATED LIBRARIES, EXPANDED INTO MEASURED COVERAGE
Scenario libraries per use case
PROCEDURALLY GENERATED — YOU SET THE DISTRIBUTION
lightingdemographicsweatherocclusion+
AUTO-ANNOTATED BY CONSTRUCTION
Measured coverage of the operational domain
03 Scale & iteration
One configuration. As much data as you need.
A validated scenario is a configuration, not a finished dataset. Anyverse turns it into data at production scale — thousands to millions of physically simulated samples, generated in the cloud and regenerated as your model's needs change.
Procedural generation at scale
Scenarios expand automatically into thousands of physically simulated variants — hundreds to millions of samples, with no manual scene work.
Cloud-native production
Generation runs as distributed cloud workloads: production-scale datasets, stored in the cloud and accessible to your whole team, with no local render infrastructure.
Dataset iteration
Every dataset is a configuration, not a one-off. Change a parameter, shift a distribution, and generate another augmented set. Test hypotheses about your data as fast as you test hypotheses about your model.
Every dataset is reproducible by design — the same scenario and configuration always produce the same data, so a validation result can be re-run, audited, and defended.
Platform capabilities
Everything around the core, built for production.
The three pillars above define what makes Anyverse data trustworthy. These capabilities make it practical to use in production, every day.
Streamlined workflow & UI
Configure scenarios, sensors, variability, and outputs from a single interface. No rendering expertise or coding required.
Physics first, gen AI on top
Generative AI adds realism on top of physically simulated scenes, never replacing the geometry, or ground truth, which stay simulation-accurate.
Assets & modeling
Compose scenes from a comprehensive 3D asset library or have us replicate your exact hardware, scenarios, and prototypes to spec.
Pipeline integration
Outputs adapt to your ontology and labeling specification, delivered in the formats your ML stack expects.
Streamlined workflow & UI
Configure scenarios, sensors, variability, and outputs from a single interface. No rendering expertise or coding required.
Physics first, gen AI on top
Generative AI adds realism on top of physically simulated scenes, never replacing the geometry, or ground truth, which stay simulation-accurate.
Assets & modeling
Compose scenes from a comprehensive 3D asset library or have us replicate your exact hardware, scenarios, and prototypes to spec.
Pipeline integration
Outputs adapt to your ontology and labeling specification, delivered in the formats your ML stack expects.
Trust & Experience
Physics-based simulation isn't new to us. It's what we were built on.
Our team brings 25 years of experience developing state-of-the-art rendering and simulation technologies, used by engineers, scientists, and visual effects studios for over two decades. That is the lineage our synthetic data is built on.
See the technology on your use case
Tell us your sensor stack and your target scenarios — we'll show you what the data looks like.
