We also introduce a novel focus-picking loss function to improve classification accuracy by encouraging FocusNet to focus on the most confusing classes.
Recognizing places using Lidar in large-scale environments is challenging due to the sparse nature of point cloud data.
Experimental results show that, our algorithm can efficiently restore color images with higher SNR and richer details from the mono-color image pairs, and achieves good performance in solving the color bleeding problem.
As the acquired multispectral and multimodal data are generally misaligned due to the alternation or movement of the image device, the image registration procedure is necessary.
In this paper, a new measure, namely normalized total gradient (NTG), is proposed for multispectral image registration.