This paper presents F-Siamese Tracker, a novel approach for single object
tracking prominently characterized by more robustly integrating 2D and 3D
information to reduce redundant search space. A main challenge in 3D single
object tracking is how to reduce search space for generating appropriate 3D
candidates. Instead of solely relying on 3D proposals, firstly, our method
leverages the Siamese network applied on RGB images to produce 2D region
proposals which are then extruded into 3D viewing frustums. Besides, we perform
an online accuracy validation on the 3D frustum to generate refined point cloud
searching space, which can be embedded directly into the existing 3D tracking
backbone. For efficiency, our approach gains better performance with fewer
candidates by reducing search space. In addition, benefited from introducing
the online accuracy validation, for occasional cases with strong occlusions or
very sparse points, our approach can still achieve high precision, even when
the 2D Siamese tracker loses the target. This approach allows us to set a new
state-of-the-art in 3D single object tracking by a significant margin on a
sparse outdoor dataset (KITTI tracking). Moreover, experiments on 2D single
object tracking show that our framework boosts 2D tracking performance as well.

本文介绍了一种新型的 F-Siamese Tracker 方法，它通过更加稳健地整合 2D 和 3D 信息来减少冗余搜索空间，从而在 KITTI 跟踪数据集上显著提高了 3D 单目标跟踪和 2D 单目标跟踪性能。