基于卷积神经网络的三维物体检测方法

A Convolutional Neural Network-Based Method for 3D Object Detection

提出了一种新的三维物体检测方法.在物体定位部分,采用随机采样一致和欧式聚类算法分割三维物体点云以减少计算量;在物体识别部分,将物体点云转化为深度图像,利用k-Means聚类算法学习卷积核,利用卷积网络提取卷积特征,从而提高图像的识别率,并在2个公开的三维物体数据集上对所提出的特征提取算法进行测试.结果表明,与传统的点云特征提取方法相比,基于卷积网络的特征提取方法的识别率较高.

We used random sample consensus and distance cluster to segment object instead of sliding windows.In recognition step,we designed a new algorithm to extract point cloud feature.Firstly,the point cloud of objects was converted to depth map,then k-Means is applied to learn features from random patches.The learned feature can be used as the convolutional neural network(CNN)filters and convolved over the input image to extract convolutional feature.The presented method was tested by using two public datasets.The results showed that feature learned by single layer CNN can achieve higher recognition rate than artificially designed feature.