.png){kind=icon}
返回
Wearable Depth Camera: Monocular Depth Estimation via Sparse Optimization Under Weak Supervision
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2018.2857703.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Depth estimation is essential for many human-object interaction tasks. Despite its advantages, traditional depth sensors, including Kinect or depth camera, are always not wearable-friendly due to several critical drawbacks, such as over-size or over-weight. Monocular camera, on the other hand, provides a promising solution with limited burden to users and attracts more and more attentions in the literature. In this paper, we propose a depth estimation method with monocular camera. Our main idea lies in the weak-supervised learning model of monocular depth estimation based on left and right consistency. To learn an accurate depth estimation, on our training step, we employ LiDAR data, which are generated by laser radar with very high depth accuracy, to semi-supervise the learning scheme. We train our network on ResNet and propose a new penalty function, which takes into account the LiDAR depth loss in training. Compared with several state-of-the-art monocular camera depth estimators, our proposed method obtains the highest depth accuracy.
Keyword:
Wearable devices
depth estimation
deep learning
weak supervision
sparse optimization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Real-Time Continuous Detection and Recognition of Subject-Specific Smart TV Gestures via Fusion of Depth and Inertial Sensing.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏分享收藏分享收藏On the Construction of Data Aggregation Tree With Maximizing Lifetime in Large-Scale Wireless Sensor NetworksIEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏
查看更多内容