Current 3D single object tracking approaches track the target based on a feature
comparison between the target template and the search area. However, due to the common …

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 …

Towards 3D object tracking in point clouds, a novel point-to-box network termed P2B is
proposed in an end-to-end learning manner. Our main idea is to first localize potential target …

Abstract 3D object tracking in point clouds is still a challenging problem due to the sparsity of
LiDAR points in dynamic environments. In this work, we propose a Siamese voxel-to-BEV …

Point clouds are challenging to process due to their sparsity, therefore autonomous vehicles
rely more on appearance attributes than pure geometric features. However, 3D LIDAR …

Visual object tracking has been drawing increasing attention in recent years, as a
fundamental task in computer vision. To extend the range of tracking applications …

Compared with traditional RGB-only visual tracking, few datasets have been constructed for
RGB-D tracking. In this paper, we propose ARKitTrack, a new RGB-D tracking dataset for …

RGBD object tracking is gaining momentum in computer vision research thanks to the
development of depth sensors. Although numerous RGBD trackers have been proposed …

Most correlation filter (CF) based trackers utilize the circulant structure of the training data to
learn a linear filter that best regresses this data to a hand-crafted target response. These …

Feature fusion and similarity computation are two core problems in 3D object tracking,
especially for object tracking using sparse and disordered point clouds. Feature fusion could …