Faster 3D Reconstruction by Fusing 2D Object Detection and Self-Supervised Monocular Depth Estimation

Abstract

In recent years, real-time 3D reconstruction has gained popularity among many researchers due to its increasing applications. 3D reconstruction methods based on depth estimation have a large number of invalid areas for reconstruction in autonomous driving. To further improve the real-time performance of 3D reconstruction, we propose a novel approach to reduce the consumption of computational resources by extracting significant regions of depth maps. First, binary masks that extract significant regions are generated by a 2D object detection model. Then, we design the TRDE module to extract target regions in generated depth maps. Finally, qualitative and quantitative results on KITTI dataset demonstrate that our approach can perform depth maps optimization and reduce computational resources consumed during the 3D reconstruction. As a result, our method achieves faster 3D reconstruction by fusing 2D object detection and self-supervised monocular depth estimation.