Deep learning has recently gained popularity achieving state-of-the-art performance in tasks
involving text, sound, or image processing. Due to its outstanding performance, there have …

Point cloud is a widely used 3D data form, which can be produced by depth sensors, such
as Light Detection and Ranging (LIDAR) and RGB-D cameras. Being unordered and …

Unlike images which are represented in regular dense grids, 3D point clouds are irregular
and unordered, hence applying convolution on them can be difficult. In this paper, we extend …

Existing knowledge distillation methods focus on convolutional neural networks (CNNs),
where the input samples like images lie in a grid domain, and have largely overlooked …

Point cloud is an important type of geometric data structure. Due to its irregular format, most
researchers transform such data to regular 3D voxel grids or collections of images. This …

Matching local geometric features on real-world depth images is a challenging task due to
the noisy, low-resolution, and incomplete nature of 3D scan data. These difficulties limit the …