基于位置依赖的密集融合的6D位姿估计方法

6D Pose Estimation Method Based on Position Dependent Dense Fusion

基于RGBD的6D位姿估计方法的一个关键问题是如何进行彩色特征信息和深度特征信息的融合。先前的工作采用密集融合的方法,主要关注的是局部特征和全连接层提取的全局特征,忽略了远距离像素间的位置依赖关系。文章提出通过捕获像素间的位置关系,并将其与彩色特征图和几何特征图进行密集融合,最后逐像素预测物体的6D位姿。实验结果表明,该文的方法相比其他方法在YCB-Video数据集上获得更优的结果。

One of the key problems of the 6D pose estimation method based on RGBD is how to fuse the color feature information and depth feature information.Previous work used dense fusion method,mainly focused on local features and global features extracted from fully connected layer,ignoring the position dependence between remote pixels.The article proposes that by capturing the positional relationship between pixels and intensively fusing it with the color feature map and geometric feature map,the 6D pose of the object is predicted pixel by pixel.Experimental results show that the proposed method achieves better results than other methods on YCB-Video dataset.