论文标题
信任指导深度完成网络
Confidence Guided Depth Completion Network
论文作者
论文摘要
本文提出了一种图像引导的深度完成方法，以通过快速计算时间估算准确的密集深度图。所提出的网络具有两阶段的结构。第一阶段预测了第一阶段的深度图。然后，第二阶段使用置信图进一步完善了第一个深度图。第二阶段由两层组成，每个层都集中在不同的区域上，并生成了精致的深度图和置信图。最终的深度图是通过使用相应的置信图组合第二阶段的两个深度图获得的。与Kitti深度完成在线排行榜上排名最高的模型相比，建议的模型显示出更快的计算时间和竞争性能。
The paper proposes an image-guided depth completion method to estimate accurate dense depth maps with fast computation time. The proposed network has two-stage structure. The first stage predicts a first depth map. Then, the second stage further refines the first depth map using the confidence maps. The second stage consists of two layers, each of which focuses on different regions and generates a refined depth map and a confidence map. The final depth map is obtained by combining two depth maps from the second stage using the corresponding confidence maps. Compared with the top-ranked models on the KITTI depth completion online leaderboard, the proposed model shows much faster computation time and competitive performance.
