一种多尺度特征融合的多模态三维点云配准模型

A multil-modal 3D point cloud registration model with multi-scale feature fusion

针对三维点云配准中现有描述符提取方法可能导致点云结构信息不显著以及点云数据细节丢失的问题,提出了一种多尺度特征融合的多模态三维点云配准模型(Multi-scale Feature Fusion,MSFNet)。首先,在编码器中采用基于稀疏卷积的通道注意力模块(Channel Attention Module Based On Sparse Convolution,SCCAM)使得该模型能够自适应地关注点云的特征结构;然后,利用多尺度空间点云编码结构(Multi-scale Spatial Point Cloud Encoding,MSPCE)提取并有效融合不同尺度下的点云特征,从而增加点云描述符的感受野;最后,利用多模态特征融合模块对编码器提取的点云特征与图片特征进行融合,并将其送入解码器进行监督训练,以生成最终的点云描述符。采用特征匹配召回率(Feature-Match Recall,FMR)作为评价指标,在数据集3DMatch上进行实验。实验结果表明MSFNet网络其召回率精度达到了98.4%,与IMFNet(Interpretable Multimodal Fusion)网络相比,提升了0.8%。

A multi-modal 3D point cloud registration model with multi-scale feature fusion(multi-scale feature fusion,MSFNet)is proposed to address the problems that existing descriptor extraction methods in 3D point cloud registration may result in insignificant point cloud structure information and loss of point cloud data details.Firstly,a channel attention module based on sparse convolution(channel attention module based on sparse convolution,SCCAM)is employed in the encoder to enable the model to adaptively focus on the feature structure of the point cloud.Then,a multi-scale spatial point cloud encoding structure(multi-scale spatial point cloud encoding,MSPCE)is used to extract and effectively fuse point cloud features at different scales,thereby increasing the receptive field of the point cloud descriptor.Finally,a multi-modal feature fusion module is used to fuse the point cloud features extracted by the encoder with image features,which are then fed into the decoder for supervised training to generate the final point cloud descriptor.Feature-Match Recall(FMR)is employed as an evaluation metric to conduct experiments on the 3DMatch dataset.The experimental results show that the recall accuracy of the MSFNet achieves 98.4%,which is 0.8%higher than that of the IMFNet(Interpretable Multimodal Fusion).