Long-tail scenarios need rare objects
AV stacks must handle construction barrels, dropped cargo, scattered debris, stalled vehicles. Building varied assets for rare, safety-critical scenarios is prohibitively expensive by hand.
SimReady Assets for Self-Driving
Autonomous vehicle stacks need realistic road environments, vehicles, cyclists, pedestrians, road debris, infrastructure. Rigyd converts 3D models, images, or text into physics-enabled assets with collision geometry and semantic labels for CARLA, DRIVE Sim, and Omniverse.
Updated 2026-05-08
The problem
Why existing workflows fall short.
Semantic labels and physics rarely coexist
Most AV asset libraries focus on either visual fidelity or simplified physics, not both. Full-stack testing needs perception-ready labels AND accurate collision behavior on the same object.
Scenario coverage is a data problem
Reaching 99.9% scenario coverage takes thousands of object variations. Manual asset creation caps scenario diversity well before the coverage level regulators expect.
How Rigyd helps
Automate the hard parts.
Semantically labeled OpenUSD
Each asset ships with physics properties AND perception-ready semantic labels, enabling combined camera, lidar, and collision evaluation in CARLA, NVIDIA DRIVE Sim, or Omniverse.
Edge-case object library at scale
Upload reference scans, 3D catalogs of road debris, traffic cones, and construction objects and get physics-enabled versions in minutes, unlocking long-tail scenario coverage.
Verified for collision modelling
Mass, friction, and restitution values are calibrated for collision modeling at highway speeds, essential for realistic pre-crash, near-miss, and emergency-maneuver scenarios.
99.9%
scenario coverage needed for safe AV deployment
97%
cost reduction vs manual asset preparation
$370K
saved per 1,000-object scenario library
FAQ
Frequently asked questions
Does Rigyd output semantically labeled assets for AV stacks?
Yes. Each asset includes physics properties AND perception-ready semantic labels, enabling combined camera, lidar, and collision evaluation in CARLA, NVIDIA DRIVE Sim, or Omniverse without a separate annotation step.
Can Rigyd produce long-tail scenario objects at scale?
Yes. Enterprise API converts reference scans and 3D catalogs of debris, cones, construction objects, and stalled vehicles, the long-tail objects needed for the 99.9% coverage AV regulators expect.
Are physics values calibrated for highway-speed collision modeling?
Yes. Mass, friction, and restitution are tuned for collision modeling at AV-relevant speeds, essential for realistic pre-crash, near-miss, and emergency-maneuver scenario testing.
Does Rigyd support CARLA, NVIDIA DRIVE Sim, and Omniverse for AV workflows?
Yes. OpenUSD output is native to NVIDIA DRIVE Sim and Omniverse and drops in without conversion. For CARLA, the OpenUSD output can be exported to .fbx with embedded physics metadata using community USD→FBX tooling, which CARLA's asset importer then consumes. Semantic labels propagate via SemanticsAPI on the USD path, enabling perception-model training across the AV simulation stack from one source.
Can Rigyd generate assets for adverse weather or low-visibility scenarios?
Yes. Material classification handles wet, snow-covered, and dirty variants of standard road objects (cones, signs, debris) with adjusted friction coefficients per condition. Rigyd doesn't generate weather effects (rain particles, fog), those are simulator runtime features, but it provides the surface-property metadata weather simulation needs to behave correctly against your scenario assets.
Scale your AV scenario library
Convert 3D models, images, or text into physically accurate, semantically labeled assets for self-driving simulation.