Dynamic Guided Network for Monocular Depth Estimation
文献类型：会议论文
| 作者 | Xing, Xiaoxia1,2 ; Cai, Yinghao2; Wang, Yanqing2; Lu, Tao2; Yang, Yiping2; Wen, Dayong2
|
| 出版日期 | 2021-05 |
| 会议日期 | Jan. 10-15, 2021 |
| 会议地点 | Milan, Italy |
| 关键词 | Depth estimation Dynamic guide filter Self-attention mechanism |
| DOI | 10.1109/ICPR48806.2021.9413264 |
| 英文摘要 | Self-attention and encoder-decoder have been widely used in the deep neural network for monocular depth estimation. The self-attention mechanism is capable of capturing long-range dependencies by computing the representation of each image position by a weighted sum of the features at all positions, while the encoder-decoder can capture detailed structural information by gradually recovering spatial information. In this work, we combine the advantages of both methods. Specifically, our proposed model, DGNet, extends EMANet by adding an effective decoder module to progressively refine the coarse depth map. In the decoder stage, we design a dynamic guided upsampling module that employs dynamically generated kernel conditioned on low-level features to guide the upsampling of the coarse depth map. Experimental results demonstrate that our method obtains higher accuracy and generates visually pleasant depth maps. |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/48782]  |
| 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 | Cai, Yinghao |
| 作者单位 | 1.University of Chinese Academy of Sciences, Beijing, China 2.Institute of Automation, Chinese Academy of Sciences, Beijing, China |
| 推荐引用方式 GB/T 7714 | Xing, Xiaoxia,Cai, Yinghao,Wang, Yanqing,et al. Dynamic Guided Network for Monocular Depth Estimation[C]. 见:. Milan, Italy. Jan. 10-15, 2021. |
入库方式： OAI收割
来源：自动化研究所
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