2019/07/13 by Lei Zhang, Weihai Chen, Zhang, Lei +7
Computer Science · Engineering · #Advanced Vision and Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image Processing Techniques and Applications #Image and Video Processing (eess.IV) #Optical measurement and interference techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1907.06071
openalex publication_date 2019/07/13 · openalex created_date 2024/04/11 · openalex updated_date 2026/07/28
Dense depth completion is essential for autonomous systems and 3D reconstruction. In this paper, a lightweight yet efficient network (S&CNet) is proposed to obtain a good trade-off between efficiency and accuracy for the dense depth completion. A dual-stream attention module (S&C enhancer) is introduced to measure both spatial-wise and the channel-wise global-range relationship of extracted features so as to improve the performance. A coarse-to-fine network is designed and the proposed S&C enhancer is plugged into the coarse estimation network between its encoder and decoder network. Experimental results demonstrate that our approach achieves competitive performance with existing works on KITTI dataset but almost four times faster. The proposed S&C enhancer can be plugged into other existing works and boost their performance significantly with a negligible additional computational cost.