# SynthRender and I-AsSET: Open-Source Framework and Dataset for Bidirectional Sim-Real Transfer in Industrial Object Perception Source: [SynthRender and I-AsSET: Open-Source Framework and Dataset for Bidirectional Sim-Real Transfer in Industrial Object Perception]() Collection index: [llms.txt]() Data updated: 2026-08-11T05:03:57.498Z Authors: Jose Moises Araya-Martinez, Thushar Tom, Adri\\'an Sanchis Reig, Pablo Rey Valiente, Jens Lambrecht, J\\"org Kr\\"uger Published: 2026-02-24T17:42:34.000Z ## Abstract Object perception is fundamental for tasks such as robotic material handling and quality inspection. However, modern supervised deep-learning models require large annotated datasets for robust automation under semi-uncontrolled conditions; a major barrier for widespread deployment with proprietary industrial parts. We address this through an integrated framework combining synthetic data generation and structured empirical evaluation for systematic investigation of bidirectional sim-to-real transfer. Our method integrates 2D-to-3D Reality-to-Simulation techniques for 3D asset creation from physical parts with programmatic Guided Domain Randomization (GDR) via SynthRender, an open-source synthetic image generation framework. Structured ablation studies across multiple benchmarks quantify the impact of individual rendering design choices, yielding practical guidelines for data-efficient synthetic training. To support evaluation under realistic industrial conditions, we introduce Industrial Assets for Sim-to-Real Evaluation and Transfer (I-AsSET), a 32-class dataset with diverse textures, intra-class variation, strong inter-class similarities, and 19,672 annotations, providing both CAD models and reconstructed meshes for bidirectional sim-to-real benchmarking. Across three industrial benchmarks, the proposed framework achieves highly competitive performance, reaching 98.7% mAP@50 on a public robotics dataset, 97.9% mAP@50 on an automotive benchmark, and 95.1% mAP@50 on I-AsSET. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Object Detection on Robotics](): Object Detection; Robotics; mAP@50: 99.3. - [Object Detection on Automotive](): Object Detection; Automotive; mAP@50-95: 88.1. - [Object Detection on IRIS](): Object Detection; Iris; mAP@50: 95.3. ## Original sources and implementation [Original paper]() [PDF]()