The two-dimensional multiwindow S-method for radar imaging applications is proposed. It represents a combined technique that uses the standard S-method and the multiple windows approach based on the two-dimensional Hermite functions. The proposed method provides significant improvement of radar image concentration in comparison with the standard S-method. Also, it does not require an additional post-processing algorithm. The efficiency of the proposed method is demonstrated through various examples.

The most recent 3D object detectors for point clouds rely on the coarse voxel-based representation rather than the accurate point-based representation due to a higher box recall in the voxel-based Region Proposal Network (RPN). However, the detection accuracy is severely restricted by the information loss of pose details in the voxels. Different from considering the point cloud as voxel or point representation only, we propose a point-to-voxel feature learning approach to voxelize the point cloud with both the point-wise semantic and local spatial features, which maintains the voxel-wise features to build the high-recall voxel-based RPN and also provides the accurate point-wise features for refining the detection results. Another difficulty in object detection for point cloud is that the visible part varies a lot against the full view of object because of the perspective issues in data acquisition. To address this, we propose an attentive corner aggregation module to attentively aggregate the features of local point cloud surrounding a 3D proposal from the perspectives of eight corners in the proposal 3D bounding box. The experimental results on the competitive KITTI 3D object detection benchmark show that the proposed method achieves state-of-the-art performance.