Bridging the Reality Gap: Enhancing Synthetic Construction Data for Deep Learning Applications
Data scarcity in the construction domain limits the performance of deep neural networks, especially for computer vision tasks such as object detection and activity recognition. While synthetic data offers a scalable alternative to real-world data collection, its lack of photorealism often results in a noticeable “reality gap,” reducing the effectiveness of trained models in practical scenarios. Addressing this challenge requires innovative frameworks that can enhance the visual realism and diversity of synthetic datasets while retaining precise annotations. Synthetic Data Generation Challenges in Construction Generating high-quality synthetic datasets for construction environments presents several challenges, including complex lighting conditions, occlusions, material textures, and dynamic scenes involving workers and machinery. Traditional 3D rendering techniques fail to fully capture these contextual details, leading to unrealistic visual outputs. The lack of domain-specific re...