Self-Supervised Monocular Depth Estimation via Discrete Strategy and Uncertainty

被引：0

作者：

Zhenyu Li ^{[1
,2
]}

Junjun Jiang ^{[1
,2
]}

Xianming Liu ^{[1
,2
]}

机构：

[1] the School of Computer Science and Technology,Harbin Institute of Technology

[2] Peng Cheng Laboratory

来源：

IEEE/CAAJournalofAutomaticaSinica | 2022年 / 9卷 / 07期

基金：

中国国家自然科学基金; 中央高校基本科研业务费专项资金资助;

关键词：

D O I：

暂无

中图分类号：

TP391.41 [];

学科分类号：

080203 ;

摘要：

Dear Editor, This letter is concerned with self-supervised monocular depth estimation. To estimate uncertainty simultaneously, we propose a simple yet effective strategy to learn the uncertainty for selfsupervised monocular depth estimation with the discrete strategy that explicitly associates the prediction and the uncertainty to train the networks. Furthermore, we propose the uncertainty-guided feature fusion module to fully utilize the uncertainty information.

引用

页码：1307 / 1310

页数：4

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