ShanghaiTech University Knowledge Management System
| HiddenPose: Non-Line-of-Sight 3D Human Pose Estimation | |
| 2022-08-05 | |
| 会议录名称 | 2022 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL PHOTOGRAPHY (ICCP)
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| ISSN | 2164-9774 |
| 发表状态 | 已发表 |
| DOI | 10.1109/ICCP54855.2022.9887660 |
| 摘要 | Nearly all existing human pose estimation techniques address the problem under the line-of-sight (LOS) setting. Many real-life applications such as rescue missions and autonomous driving, in contrast, require estimating the pose of hidden subjects. In this paper, we present a non-line-of-sight (NLOS) pose estimator, which produces a skeletal representation of hidden human poses. A brute-force approach would first conduct albedo reconstruction of a hidden subject and then apply LOS pose estimation. We show that such an implementation does not effectively exploit features unique to NLOS and subsequently yields artifacts such as missing joints. We instead first generate a comprehensive NLOS human pose dataset of 19 subjects under 9 motions. We then present a spatially aware deep learning technique based on convolutional neural networks that explicitly employ NLOS features. Comprehensive experiments on both synthetic and real data show that our new estimator is both effective and robust and can be seamlessly integrated into learning-based NLOS scene reconstruction. Our HiddenPose transient dataset contains synthetic transients with ground-truths of the volumes and the joints and real-world transients captured from our NLOS imaging system. Extensive assessments demonstrate that the HiddenPose transient dataset is valuable for effective NLOS research. We will make our data and code publicly available. |
| 关键词 | Computational Photography |
| 会议地点 | Pasadena, CA, USA |
| 会议日期 | 1-5 Aug. 2022 |
| URL | 查看原文 |
| 收录类别 | EI ; CPCI-S |
| 来源库 | IEEE |
| 引用统计 | 正在获取...
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| 文献类型 | 会议论文 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/243111 |
| 专题 | 信息科学与技术学院_硕士生 信息科学与技术学院_PI研究组_虞晶怡组 信息科学与技术学院_本科生 |
| 通讯作者 | Yu, Jingyi; Li, Shiying |
| 作者单位 | 1.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China; 2.ShanghaiTech Univ, Shanghai Engn Res Ctr Intelligent Vis & Imaging, Shanghai 201210, Peoples R China |
| 第一作者单位 | 信息科学与技术学院 |
| 通讯作者单位 | 信息科学与技术学院; 上海科技大学 |
| 第一作者的第一单位 | 信息科学与技术学院 |
| 推荐引用方式 GB/T 7714 | Liu, Ping,Yu, Yanhua,Pan, Zhengqing,et al. HiddenPose: Non-Line-of-Sight 3D Human Pose Estimation[C],2022. |
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