Explainable Artificial intelligence For Magnetic Resonance Imaging Aging Brainprints Grounds and Challenges

A well-known difficulty in processing 3D point cloud is due to its unordered nature. The state-of-the-art 3D computer vision detection methods are mainly use voxels to extract point cloud features. However, a limitation of point cloud voxelization is that some features of point cloud surface cannot be fully described. To address this problem, we propose an adaptive sorting approach for voxel-based point cloud. It adaptively ranks point cloud within voxels at first and then goes on to ex- tract voxel features using a multilayer perceptron, called PAS- Net (Points- Adaptive ?Sorting-Net). The resulting method is plug-and-play and can be easily deployed in any voxel- based detection model. The proposed model was tested on the KITTI benchmark suit where a 2% ? 7% improvement of the detection accuracy is achieved compared with other mod- els using different point feature extraction methods.