一种基于三维几何特征的刚体目标分割方法

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标题:Embedding 3D Geometric Features for Rigid Object Part Segmentation

作者:Yafei Song,Xiaowu Chen,Jia Li,Qinping Zhao

来源:International Conference on Computer Vision (ICCV 2017)

播音员:zzq

编译:陈世浪 周平(61)

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摘要

目标分割是计算机视觉中一个具有挑战性和基础性的问题。目标分割的困难主要是由于不同的视角、姿态和拓扑结构引起的。这些困难的主要原因是一个特定的对象是一个三维模型,而不是一个二维图形。因此,我们推测不仅二维的外观特征有作用,而且三维的几何特征同样是有用的。

考虑到这一点,作者提出了双通道的FCN方法。一个名为AppNet的通道是从输入图像提取二维外观特征。另一个名为GeoNet的通道则是提取三维几何特征。但是,输入仅仅只是一个图像。为此,作者设计了一个名为VolNet的网络,这种网络基于二维卷积神经网络并从三维体重提取三维的几何特征。作者之后采用了教师-学生策略,VolNet教GeoNet如何从图像中提取三维几何特征。为了完成这个教学过程,作者使用三维模型来合成训练数据。每个训练脚本由图像及其相应的体素构成。作者进一步提出了一种基于视角的体素化算法来实现对齐。

实验结果验证了作者的猜想和提出的二通道CNN、VolNet。

图1 算法框架

图2 算法效果对比图

Abstract

Object part segmentation is a challenging and fundamental problem in computer vision. Its difficulties may be caused by the varying viewpoints, poses, and topological structures, which can be attributed to an essential reason, i.e., a specific object is a 3D model rather than a 2D figure.Therefore, we conjecture that not only 2D appearance features but also 3D geometric features could be helpful.

With this in mind, we propose a 2-stream FCN. One stream, named AppNet, is to extract 2D appearance features from the input image. The other stream, named GeoNet, is to extract 3D geometric features. However, the problem is that the input is just an image. To this end, we design a 2D-convolution based CNN structure to extract 3D geometric features from 3D volume, which is named VolNet. Then a teacher-student strategy is adopted and VolNet teaches GeoNet how to extract 3D geometric features from an image. To perform this teaching process, we synthesize training data using 3D models. Each training sample consists of an image and its corresponding volume. A perspective voxelization algorithm is further proposed to align them.

Experimental results verify our conjecture and the effectiveness of both the proposed 2-stream CNN and VolNet.

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  • 原文链接https://kuaibao.qq.com/s/20180722A06YUY00?refer=cp_1026
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