VIDAR: Data Quality Improvement for Monocular 3D Reconstruction through In-situ Visual Interaction

Han Gao, Yating Liu, Fang Cao, Hao Wu, Fengyuan Xu, Sheng Zhong

Abstract: 3D reconstruction based on monocular videos has attracted wide attention, and existing reconstruction methods usually work in a reconstruction-after-scanning manner. However, these methods suffer from insufficient data collection problems due to the lack of effective guidance for users during the scanning process, which affects reconstruction quality. We propose VIDAR, which visually guides users with the streaming incremental reconstructed mesh in data collection for monocular 3D reconstruction. We propose an incremental mesh extraction algorithm to achieve lossless fusion of streaming incremental mesh data via slice-style management for guidance quality. We also design an incremental mesh rendering algorithm to achieve precise memory reallocation by updating the buffer in a fill-in-the-blank pattern for guidance efficiency. Besides, we introduce several optimizations on data transmission and human-computer interaction to improve the overall system performance. The experiment results on real-world scenes show that VIDAR efficiently delivers high-quality visual guidance and outperforms the non-interactive data collection methods for scene reconstruction.