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：摘要：本文提出SynTable，一种基于NVIDIA Isaac Sim Replicator Composer构建的统一且灵活的Python数据集生成工具，专门用于生成杂乱桌面场景中未见过物体非模态实例分割的高质量合成数据集。该数据集生成工具可渲染包含物体网格、材质、纹理、光照和背景的复杂三维场景。系统支持按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，从而在确保数据集质量与精度的前提下免除了人工标注流程。本文详细阐述了设计目标、框架架构及工具性能，并通过光线追踪技术展示了使用SynTable生成的样本数据集对前沿模型UOAIS-Net进行训练的效果。在OSD-Amodal数据集上的评估结果表明，该工具显著提升了Sim-to-Real（仿真到现实）迁移性能。我们将此开源、易用且具备照片级真实感的数据集生成工具贡献给学术界，以推动深度学习与合成数据生成领域的研究发展。