3 accepted papers
"Learning efficient representations of local features is a key challenge in feature volume-based 3D neural mapping, especially in large-scale environments. In this paper, we introduce Decomposition-based Neural Mapping (DNMap), a storage-efficient large-scale 3D mapping method that employs a discret…
LiDAR super-resolution is a relatively new problem in which we seek to fill in the blanks between measured points when a low-resolution LiDAR is given, making a high-resolution LiDAR or even a resolution-free LiDAR. Recently, several research works have been reported regarding LiDAR super-resolution
In this paper, a new deep learning network named as graph-based point tracker (GPT) is proposed for 3D object tracking in point clouds. GPT is not based on Siamese network applied to template and search area, but it is based on the transfer of target clue from the template to the search area. GPT is…