Edge-Aware Monocular Dense Depth Estimation with Morphology | |
Zhi, Li1,2![]() ![]() ![]() ![]() ![]() | |
2021 | |
会议名称 | 25th International Conference on Pattern Recognition (ICPR) |
会议日期 | 2021-1 |
会议地点 | Milan, Italy |
摘要 | Dense depth maps play an important role in Computer Vision and AR (Augmented Reality). For CV applications, a dense depth map is the cornerstone of 3D reconstruction allowing real objects to be precisely displayed in the computer. And Dense depth maps can handle correct occlusion relationships between virtual content and real objects for better user experience in AR. However, the complicated computation limits the development of computing dense depth maps. We present a novel algorithm that produces low latency, spatio-temporally smooth dense depth maps using only a CPU. The depth maps exhibit sharp discontinuities at depth edges in low computational complexity ways. Our algorithm obtains the sparse SLAM reconstruction first, then extracts coarse depth edges from a down-sampled RGB image by morphology operations. Next, we thin the depth edges and align them with image edges. Finally, an effective initialization scheme and an improved optimization solver are adopted to accelerate convergence. We evaluate our proposal quantitatively and the result shows improvements on the accuracy of depth map with respect to other state-of-the-art and baseline techniques. |
收录类别 | EI |
七大方向——子方向分类 | 三维视觉 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44313 |
专题 | 综合信息系统研究中心_视知觉融合及其应用 |
通讯作者 | Xiao yang, Zhu |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhi, Li,Xiao yang, Zhu,Hai tao, Yu,et al. Edge-Aware Monocular Dense Depth Estimation with Morphology[C],2021. |
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ICPR收录版903.pdf(9850KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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