Panoptic Segmentation using Synthetic and Real Data

TL;DR

This paper introduces a pipeline for generating synthetic labeled images from 3D models to improve panoptic segmentation in industrial environments, reducing the need for extensive real labeled data.

Contribution

The authors propose a novel pipeline for creating synthetic datasets from 3D models, combined with minimal real data, to enhance panoptic segmentation performance in industrial scenarios.

Findings

Synthetic data significantly reduces the need for real labeled images.

The combined synthetic and real data approach improves segmentation accuracy.

Experiments demonstrate effective domain adaptation with less real data.

Abstract

Being able to understand the relations between the user and the surrounding environment is instrumental to assist users in a worksite. For instance, understanding which objects a user is interacting with from images and video collected through a wearable device can be useful to inform the worker on the usage of specific objects in order to improve productivity and prevent accidents. Despite modern vision systems can rely on advanced algorithms for object detection, semantic and panoptic segmentation, these methods still require large quantities of domain-specific labeled data, which can be difficult to obtain in industrial scenarios. Motivated by this observation, we propose a pipeline which allows to generate synthetic images from 3D models of real environments and real objects. The generated images are automatically labeled and hence effortless to obtain. Exploiting the proposed pipeline, we generate a dataset comprising synthetic images automatically labeled for panoptic segmentation. This set is complemented by a small number of manually labeled real images for fine-tuning. Experiments show that the use of synthetic images allows to drastically reduce the number of real images needed to obtain reasonable panoptic segmentation performance.