Enhancing point cloud semantic segmentation in the data‐scarce domain of industrial plants through synthetic data

Abstract

Digitizing existing structures is essential for applying digital methods in architecture, engineering, and construction. However, the adoption of data-driven techniques for transforming point cloud data into useful digital models faces challenges, particularly in the industrial domain, where ground truth datasets for training are scarce. This paper investigates a solution leveraging synthetic data to train data-driven models effectively. In the investigated industrial domain, the complex geometry of building elements often leads to occlusions, limiting the effectiveness of conventional sampling-based synthetic data generation methods. Our approach proposes the automatic generation of realistic and semantically enriched ground truth data using surface-based sampling methods and laser scan simulation on industry-standard 3D models. In the presented experiments, we use a neural network for point cloud semantic segmentation to demonstrate that compared to sampling-based alternatives, simulation-based synthetic data significantly improve mean class intersection over union performance on real point cloud data, achieving up to 7% absolute increase.