SynthRender 和 IRIS：用于工业物体感知双向仿真-现实迁移的开源框架和数据集
SynthRender and I-AsSET: Open-Source Framework and Dataset for Bidirectional Sim-Real Transfer in Industrial Object Perception
AI总结：
本文提出SynthRender和IRIS，通过合成数据生成与结构化评估，系统研究双向仿真-现实迁移，提供32类数据集和CAD模型，实现高效合成训练与工业应用。
摘要：
物体感知对于机器人物料搬运和质量检测等任务至关重要。然而，现代监督深度学习模型需要大量标注数据以在半受控条件下实现稳健自动化；这是在自有工业部件上广泛应用的主要障碍。我们通过整合合成数据生成和结构化经验评估的框架，系统研究双向仿真-现实迁移。我们的方法结合2D到3D的现实到仿真技术，通过SynthRender开源框架的程序化引导域随机化（GDR）从物理部件创建3D资产。跨多个基准的结构化消融研究量化了单个渲染设计选择的影响，得出实用的高效合成训练指南。为支持在现实工业条件下的评估，我们引入工业现实-仿真图像集（IRIS），包含32类，具有多样的纹理、类内变化、强类间相似性，并有19,672个注释，提供CAD模型和重建网格用于双向仿真-现实基准测试。在三个工业基准上，所提框架实现了高度竞争性的性能，达到99.1% mAP@50在公开机器人数据集、98.3% mAP@50在汽车基准和95.3% mAP@50在IRIS上。
英文摘要：
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.
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