3 accepted papers
Point cloud oversegmentation is a challenging task since it needs to produce perceptually meaningful partitions (i.e., superpoints) of a point cloud. Most existing oversegmentation methods cannot efficiently generate superpoints from large-scale LiDAR point clouds due to complex and inefficient proc…
"Siamese network based trackers formulate 3D single object tracking as cross-correlation learning between point features of a template and a search area. Due to the large appearance variation between the template and search area during tracking, how to learn the robust cross correlation between them…
3D instance segmentation is a challenging task due to the complex local geometric structures of objects in point clouds. In this paper, we propose a learning-based superpoint graph cut method that explicitly learns the local geometric structures of the point cloud for 3D instance segmentation. Speci…