Enhanced 3D Scene Reconstruction with Semantic Understanding Using Synthetic Data and Deep Learning

Abstract

This research presents an advanced pipeline for 2D and 3D scene reconstruction with semantic understanding. The synthetic data generation, semantic segmentation, and depth estimation are leveraged to create high-fidelity reconstructions. The pipeline is designed to enhance the accuracy and detail of both 2D and 3D models, ensuring better representation of complex environments. A custom DeepLabV3-based segmentation model, trained on synthetic data with noiseresistant techniques, is used to enhance semantic accuracy. This model is optimized to handle varying conditions and complex scene layouts, improving overall performance. Furthermore, a novel algorithm integrating semantic segmentation with geometric depth reconstruction is proposed. This integration results in more accurate 3D reconstructions by combining the benefits of both segmentation and depth mapping. Experimental results show that improvements in disparity accuracy, segmentation precision, and depth map quality are demonstrated. The methods used in this research are shown to reduce errors and enhance reliability in real-world applications. A detailed analysis of these improvements is provided, highlighting how the proposed approach surpasses traditional techniques. This research provides a scalable approach that can be applied to real-world 3D scene understanding tasks. The proposed solution is expected to contribute to fields such as robotics, autonomous vehicles, and augmented reality