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An approach of point cloud denoising based on improved bilateral filtering

机译：基于改进双边滤波的点云去噪方法

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An omnidirectional mobile platform is designed for building point cloud based on an improved filtering algorithm which is employed to handle the depth image. First, the mobile platform can move flexibly and the control interface is convenient to control. Then, because the traditional bilateral filtering algorithm is time-consuming and inefficient, a novel method is proposed which called local bilateral filtering (LBF). LBF is applied to process depth image obtained by the Kinect sensor. The results show that the effect of removing noise is improved comparing with the bilateral filtering. In the condition of off-line, the color images and processed images are used to build point clouds. Finally, experimental results demonstrate that our method improves the speed of processing time of depth image and the effect of point cloud which has been built.
机译：基于改进的滤波算法，设计了一种用于构建点云的全向移动平台，该算法用于处理深度图像。首先，移动平台可以灵活移动，并且控制界面易于控制。然后，由于传统的双边过滤算法既费时又效率低下，因此提出了一种新的方法，称为局部双边过滤（LBF）。 LBF应用于通过Kinect传感器获得的处理深度图像。结果表明，与双边滤波相比，去噪效果有所提高。在离线状态下，彩色图像和处理后的图像用于建立点云。最后，实验结果表明，该方法提高了深度图像的处理速度，并提高了点云的效果。

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