点云
噪音(视频)
计算机科学
镜面反射
激光雷达
计算机视觉
人工智能
不透明度
滤波器(信号处理)
测距
遥感
反射(计算机编程)
RGB颜色模型
光学
地质学
图像(数学)
物理
电信
程序设计语言
作者
Rui Gao,Jisun Park,Xiaohang Hu,Seung-Jun Yang,Kyungeun Cho
出处
期刊:Remote Sensing
[Multidisciplinary Digital Publishing Institute]
日期:2021-08-04
卷期号:13 (16): 3058-3058
被引量:15
摘要
Signals, such as point clouds captured by light detection and ranging sensors, are often affected by highly reflective objects, including specular opaque and transparent materials, such as glass, mirrors, and polished metal, which produce reflection artifacts, thereby degrading the performance of associated computer vision techniques. In traditional noise filtering methods for point clouds, noise is detected by considering the distribution of the neighboring points. However, noise generated by reflected areas is quite dense and cannot be removed by considering the point distribution. Therefore, this paper proposes a noise removal method to detect dense noise points caused by reflected objects using multi-position sensing data comparison. The proposed method is divided into three steps. First, the point cloud data are converted to range images of depth and reflective intensity. Second, the reflected area is detected using a sliding window on two converted range images. Finally, noise is filtered by comparing it with the neighbor sensor data between the detected reflected areas. Experiment results demonstrate that, unlike conventional methods, the proposed method can better filter dense and large-scale noise caused by reflective objects. In future work, we will attempt to add the RGB image to improve the accuracy of noise detection.
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