深度图像的法向指导GPU滤波

Guided normal GPU filtering of depth images

深度相机获取深度图像由于硬件精度问题,往往会丢失大量细节信息。因此,对深度图像的滤波,已经成为深度视觉领域一个重要的课题。然而,现阶段大多数滤波的方法对于深度图像中的尖锐特征保留能力不足,往往会出现过光滑现象。针对深度图像滤波中的尖锐特征难以保留的问题,提出了一种新的深度图像的联合双边滤波方法。首先求解深度图像每个像素的法向,以投票的方式对法向的权重进行计算以进行联合双边滤波,最后根据滤波后的法向更新顶点坐标。该方法引入了高精度的纹理作为指导信息,能获取更可信的滤波效果。另外,该方法基于点云的局部信息,不需要求解很大的矩阵,且基于GPU并行,运算效率极高。实验表明,该方法能更好地保留法向的边界,具有更好的几何特征保留能力。

Depth images acquired by depth cameras generally contain noises and lose detailed geometric information.Thus,the filtering of depth images has become an important topic in both computer graphics and computer vision.However,most current filtering methods can hardly preserve the sharp features in the objects and often result in over-smoothing results.To this end,we proposed a novel joint bilateral filtering method for filtering depth images.First,we estimated the normal of each pixel in the depth image.Then we computed the weight of the normals by voting to perform joint bilateral filtering on all pixels.Finally,the vertex coordinates were updated according to the filtered normals.This method took into account the texture information with high accuracy as guidance information,which can yield more reliable filtering effects.In addition,this method was based on the local information of the point cloud,did not need to solve large matrixes,and employed GPU parallelism leading to extremely high computational efficiency.Experiments show that our method can highly preserve the edges in the normal field,thus preserving sharp features better than previous methods.