CSCE-Net: Channel-Spatial Contextual Enhancement Network for Robust Point Cloud Registration

Seeking reliable correspondences between two scenes is crucial for solving feature-based point cloud registration tasks. In this paper, we propose a novel outlier rejection network, called Channel-Spatial Contextual Enhancement Network (CSCE-Net), to obtain rich contextual information on correspondences, which can effectively remove outliers and improve the accuracy of point cloud registration. To be specific, we design a novel channel-spatial contextual (CSC) block, which is mainly composed of the Channel-Spatial Attention (CSA) layer and the Nonlocal Channel-Spatial Attention (Nonlocal CSA) layer. The CSC block is able to obtain more reliable contextual information, in which the CSA layer can selectively aggregate the mutual information between the channel and spatial dimensions. The Nonlocal CSA layer can compute feature similarity and spatial consistency for each correspondence, and the CSA layer and Nonlocal CSA layer can support each other. In addition, to improve the distinguishing ability between inliers and outliers, we present an advanced seed selection mechanism to select more dependable initial correspondences. Extensive experiments demonstrate that CSCE-Net outperforms state-of-the-art methods for outlier rejection and pose estimation tasks on public datasets with varying 3D local descriptors. In addition, the network parameters of CSCE-Net are reduced from 1.05M to 0.56M compared to the recently learning-based outlier rejection method PointDSC.