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Omni-Line-of-Sight Imaging for Holistic Shape Reconstruction
2023-04-21
状态已发表
摘要

introduce Omni-LOS, a neural computational imaging method for conducting holistic shape reconstruction (HSR) of complex objects utilizing a Single-Photon Avalanche Diode (SPAD)-based time-of-flight sensor. As illustrated in Fig. 1, our method enables new capabilities to reconstruct near-360◦ surrounding geometry of an object from a single scan spot. In such a scenario, traditional line-of-sight (LOS) imaging methods only see the front part of the object and typically fail to recover the occluded back regions. Inspired by recent advances of non-line-of-sight (NLOS) imaging techniques which have demonstrated great power to reconstruct occluded objects, Omni-LOS marries LOS and NLOS together, leveraging their complementary advantages to jointly recover the holistic shape of the object from a single scan position. The core of our method is to put the object nearby diffuse walls and augment the LOS scan in the front view with the NLOS scans from the surrounding walls, which serve as virtual “mirrors” to trap lights toward the object. Instead of separately recovering the LOS and NLOS signals, we adopt an implicit neural network to represent the object, analogous to NeRF [1] and NeTF [2]. While transients are measured along straight rays in LOS but over the spherical wavefronts in NLOS, we derive differentiable ray propagation models to simultaneously model both types of transient measurements so that the NLOS reconstruction also takes into account the direct LOS measurements and vice versa. We further develop a proof-of-concept Omni-LOS hardware prototype for real-world validation. Comprehensive experiments on various wall settings demonstrate that Omni-LOS successfully resolves shape ambiguities caused by occlusions, achieves high-fidelity 3D scan quality, and manages to recover objects of various scales and complexity.

关键词NLOS Imaging Neural Rendering Surface Reconstruction
DOIarXiv:2304.10780
相关网址查看原文
出处Arxiv
WOS记录号PPRN:65039288
WOS类目Computer Science, Software Engineering ; Engineering, Electrical& Electronic
文献类型预印本
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/348036
专题信息科学与技术学院
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_PI研究组_高盛华组
信息科学与技术学院_硕士生
信息科学与技术学院_博士生
作者单位
1.Shanghai Tech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China
2.Univ Toronto, Toronto, ON, Canada
推荐引用方式
GB/T 7714
Huang, Binbin,Peng, Xingyue,Shen, Siyuan,et al. Omni-Line-of-Sight Imaging for Holistic Shape Reconstruction. 2023.
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