Blog
Parallel Domain and NVIDIA Cosmos Transfer: Additional Scene Variation for High-Fidelity Sensor Simulation
PD Replica employs advanced scene reconstruction and rendering techniques to provide developers with realistic simulation environments suitable for open and closed-loop testing and validation of perception models. However, while PD Replica Sim can programmatically generate scene variations, there are limitations to the full breadth of environmental controls available today, and where Cosmos Transfer with an open-ended prompt can enhance the quantity of variations available.
Diagnose Model Failure Points with PD Replica Sim
ML teams can use PD Replica Sim to push their autonomous system to its limits, interrogating model weaknesses and testing its generalization more rigorously than real-world data typically allows —covering tricky edge cases, handling occlusion and orientation sensitivity, and even evaluating model performance on unseen traffic sign types.
Signs of Trust: Matching Real-World Performance with PD Replica Sim
Determine whether the performance observed on the PD Replica Sim dataset would closely mirror those obtained from the real-world dataset.
NVIDIA Cosmos vs PD Replica Sim – Which is right for you?
PD Replica Sim offers a level of control, fidelity, and determinism that NVIDIA Cosmos can’t match. In this post, we’ll break down what Cosmos is, where it shines, and where it falls short. Then we’ll compare it point-by-point with PD Replica Sim, showing you which platform is better suited for your specific needs.
Parallel Domain joins Mcity to power simulations on PD Replica digital twin
Parallel Domain, a leader in simulation and autonomous system testing, proudly announces it is now a member of the Mcity public-private mobility partnership based at the University of Michigan. Mcity partners from industry, government and academia are forward-thinking technology leaders shaping the future of autonomous mobility.
Testing for the Future: Advance Perception Via Simulation
Simulation can become transformative, with the promise of testing unlimited scenarios safely in a virtual world. However, previous approaches have struggled to achieve sufficient realism and scale to be trusted by teams to translate to real-world performance. But what if we could recreate the real world as a series of digital twins that give us the knobs and controls to test all of these permutations? It would almost be like driving one mile to generate a thousand miles of useful tests.
Closing the sim-to-real gap with PD Replica
Models trained on PD Replica environments showed a 31.4% improvement over those trained on procedural data in mean average precision (mAP)
Decoding the Streets: Boosting Road Sign Perception with Synthetic Data
Parallel Domain customers experience impressive double-digit performance gains in sign perception tasks.
Boost Optical Flow with PD Data
Parallel Domain (PD) synthetic data improves performance on optical flow tasks by 18.5% by matching flow magnitudes.
Synthetic Data Best Practices for Perception Applications
Synthetic data best practices we learned from working with many perception teams and our internal research.