Unsupervised Point Cloud Representation Learning With Deep Neural Networks: A Survey.

Point cloud data have been widely explored due to its superior accuracy and robustness under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved very impressive success in various applications such as surveillance and autonomous driving. The convergence of point cloud and DNNs has led to many deep point cloud models, largely trained under the supervision of large-scale and densely-labelled point cloud data.

Exploiting fusion architectures for multispectral pedestrian detection and segmentation.

Recent research has demonstrated that the fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e.g., daytime and nighttime).