In this work, we present SynTable, a unified and flexible Python-based dataset generator built using NVIDIA's Isaac Sim Replicator Composer for generating high-quality synthetic datasets for unseen object amodal instance segmentation of cluttered tabletop scenes. Our dataset generation tool can render a complex 3D scene containing object meshes, materials, textures, lighting, and backgrounds. Metadata, such as modal and amodal instance segmentation masks, occlusion masks, depth maps, bounding boxes, and material properties, can be generated to automatically annotate the scene according to the users' requirements. Our tool eliminates the need for manual labeling in the dataset generation process while ensuring the quality and accuracy of the dataset. In this work, we discuss our design goals, framework architecture, and the performance of our tool. We demonstrate the use of a sample dataset generated using SynTable by ray tracing for training a state-of-the-art model, UOAIS-Net. The results show significantly improved performance in Sim-to-Real transfer when evaluated on the OSD-Amodal dataset. We offer this tool as an open-source, easy-to-use, photorealistic dataset generator for advancing research in deep learning and synthetic data generation.

翻译：本文提出SynTable——一个基于NVIDIA Isaac Sim Replicator Composer构建的统一且灵活的Python数据集生成工具，旨在为杂货桌面场景中的未见物体非模态实例分割生成高质量合成数据集。该数据集生成工具能够渲染包含物体网格、材质、纹理、光照和背景的复杂三维场景，并可根据用户需求自动生成模态与非模态实例分割掩码、遮挡掩码、深度图、边界框及材质属性等元数据，从而在保证数据集质量和精度的同时，摒弃了数据集生成过程中的人工标注需求。本文阐述了设计目标、框架架构及工具性能，并展示了利用SynTable通过光线追踪生成的样本数据集训练先进模型UOAIS-Net的实例。在OSD-Amodal数据集上的评估结果表明，该工具在Sim-to-Real迁移中实现了显著性能提升。我们以开源形式提供该易用型照片级真实感数据集生成工具，以推动深度学习与合成数据生成领域的研究发展。