SynTable: A Synthetic Data Generation Pipeline for Unseen Object Amodal Instance Segmentation of Cluttered Tabletop Scenes

Zhili Ng, Haozhe Wang, Zhengshen Zhang, Francis E. H. Tay, Marcelo H Ang Jr

Keywords: Instance segmentation, Amodal Perception, Datasets for Robotic Vision

TL;DR: In this work, we present SynTable, a unified, flexible and open-source Python-based dataset generator for generating high-quality synthetic datasets for unseen object amodal instance segmentation of cluttered tabletop scenes.

Abstract: 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 complex 3D scenes containing object meshes, materials, textures, lighting, and backgrounds. Metadata, such as modal and amodal instance segmentation masks, object amodal RGBA instances, occlusion masks, depth maps, bounding boxes, and material properties can be automatically generated to 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 for training a state-of-the-art model, UOAIS-Net. Our state of the art 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. Our source code and demonstration video can be found in the supplementary materials.

Submission Number: 44