Robust Structured Light System against Subsurface Scattering Effects Achieved by CNN-based Pattern Detection and Decoding Algorithm
Robust Structured Light System against Subsurface Scattering Effects Achieved by CNN-based Pattern Detection and Decoding Algorithm
复制标题
基于 CNN 的模式检测和解码算法实现对抗次表面散射效应的鲁棒结构光系统
DOI：
10.1007/978-3-030-11009-3_22
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Hiroshi Kawasaki
中科院分区：
文献类型：
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作者：
Ryo Furukawa;Daisuke Miyazaki;Masashi Baba;Shinsaku Hiura;Hiroshi Kawasaki
为了重建真实物体的三维形状，结构光技术因其简单、稳定和精度高而被广泛应用于实际目的，如检测、工业建模、医疗诊断等。其中，单次扫描技术，只需要一个图像进行重建，成为捕捉运动物体的重要手段。单次扫描技术的一个开放问题是其不稳定性，当捕获的图案由于诸如强镜面、次表面散射、相互反射等原因而降低时。活体动物是活体扫描的重要目标之一，活体动物包括人体或器官组织，具有地下散射。本文提出了一种基于学习的方法来解决单次扫描中由次表面散射引起的模式退化问题。由于地下散射会严重模糊图案，因此需要鲁棒解码技术，我们的技术将解码过程分为两部分，如模式检测和身份识别，有效地实现了鲁棒解码；这两个部分都是由CNN实现的。为了有效地实现鲁棒模式检测，我们将直线检测问题转化为分割问题。为了实现鲁棒的ID识别，我们使用U-Net将所有区域划分为每个ID。实验表明，与现有技术相比，我们的技术对强次表面散射具有较强的鲁棒性。
To reconstruct 3D shapes of real objects, a structured-light technique has been commonly used especially for practical purposes, such as inspection, industrial modeling, medical diagnosis, etc, because of simplicity, stability and high precision. Among them, oneshot scanning technique, which requires only single image for reconstruction, becomes important for the purpose of capturing moving objects. One open problem of oneshot scanning technique is its instability, when captured pattern is degraded by some reasons, such as strong specularity, subsurface scattering, inter-reflection and so on. One of important targets for oneshot scan is live animal, which includes human body or tissue of organ, and has subsurface scattering. In this paper, we propose a learning-based approach to solve pattern degradation caused by subsurface scattering for oneshot scan. Since patterns are significantly blurred by subsurface scattering, robust decoding technique is required, which is effectively achieved by separating the decoding process into two parts, such as pattern detection and ID recognition in our technique; both parts are implemented by CNN. To efficiently achieve robust pattern detection, we convert a line detection into segmentation problem. For robust ID recognition, we segment all the region into each ID using U-Net. In the experiments, it is shown that our technique is robust against strong subsurface scattering compared to state of the art technique.