Recent works have demonstrated non-line of sight (NLOS) reconstruction by using the time-resolved signal frommultiply scattered light. These works combine ultrafast imaging systems with computation, which back-projects the recorded space-time signal to build a probabilistic map of the hidden geometry. Unfortunately, this computation is slow, becoming a bottleneck as the imaging technology improves. In this work, we propose a new back-projection technique for NLOS reconstruction, which is up to a thousand times faster than previous work, with almost no quality loss. We base on the observation that the hidden geometry probability map can be built as the intersection of the three-bounce space-time manifolds defined by the light illuminating the hidden geometry and the visible point receiving the scattered light from such hidden geometry. This allows us to pose the reconstruction of the hidden geometry as the voxelization of these space-time manifolds, which has lower theoretic complexity and is easily implementable in the GPU. We demonstrate the efficiency and quality of our technique compared against previous methods in both captured and synthetic data
Passive Non-Line-of-Sight Imaging with Light Transport Modulation
School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学技术学院）；School of Cyber Science and Technology, University of Science and Technology of China（中国科学技术大学网络空间安全学院）
Projection Convolutional Neural Networks for 1-bit CNNs via Discrete Back Propagation
Beihang University（北京航空航天大学）；China University of Mining and Technology, Beijing（中国矿业大学（北京））；Lancaster University（兰卡斯特大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；University at Buffalo（布法罗大学）
PSCT-Net: Geometry-Aware Pediatric Skull CT Reconstruction via Differentiable Back-Projection and Attention-Guided Refinement
Interdisciplinary Program in Bioengineering, Seoul National University（首尔大学生物工程跨学科项目）；Department of Transdisciplinary Medicine, Seoul National University Hospital（首尔大学医院跨学科医学系）；Department of Artificial Intelligence, Yonsei University（延世大学人工智能系）；Department of Medicine, Seoul National University College of Medicine（首尔大学医学院医学系）；Healthcare AI Research Institute, Seoul National University Hospital（首尔大学医院医疗人工智能研究所）
