3D single object tracking remains a challenging problem due to the sparsity and incompleteness of the point clouds. Existing algorithms attempt to address the challenges in two strategies. The first strategy is to learn dense geometric features based on the captured sparse point cloud. Nevertheless, it is quite a formidable task since the learned dense geometric features are with high uncertainty for depicting the shape of the target object. The other strategy is to aggregate the sparse geometric features of multiple templates to enrich the shape information, which is a routine solution in 2D tracking. However, aggregating the coarse shape representations can hardly yield a precise shape representation. Different from 2D pixels, 3D points of different frames can be directly fused by coordinate transform, i.e., shape completion. Considering that, we propose to construct a synthetic target representation composed of dense and complete point clouds depicting the target shape precisely by shape completion for robust 3D tracking. Specifically, we design a voxelized 3D tracking framework with shape completion, in which we propose a quality-aware shape completion mechanism to alleviate the adverse effect of noisy historical predictions. It enables us to effectively construct and leverage the synthetic target representation. Besides, we also develop a voxelized relation modeling module and box refinement module to improve tracking performance. Favorable performance against state-of-the-art algorithms on three benchmarks demonstrates the effectiveness and generalization ability of our method.

翻译：三维单目标跟踪由于点云的稀疏性和不完整性仍然是一个具有挑战性的问题。现有算法尝试通过两种策略应对这些挑战。第一种策略是基于捕获的稀疏点云学习密集几何特征。然而，这是一项相当艰巨的任务，因为学习到的密集几何特征在描述目标物体形状时具有高度不确定性。另一种策略是聚合多个模板的稀疏几何特征以丰富形状信息，这是二维跟踪中的常规解决方案。然而，聚合粗略的形状表征很难产生精确的形状表示。与二维像素不同，不同帧的三维点可通过坐标变换直接融合，即形状补全。基于此，我们提出通过形状补全构建由密集且完整的点云组成的合成目标表征，以精确描述目标形状，从而实现鲁棒的三维跟踪。具体而言，我们设计了一个带有形状补全的体素化三维跟踪框架，其中提出一种质量感知形状补全机制以减轻历史预测噪声带来的不利影响。这使我们能够有效构建并利用合成目标表征。此外，我们还开发了体素化关系建模模块和包围盒精化模块以提升跟踪性能。在三个基准数据集上相对于现有最先进算法的优异表现证明了我们方法的有效性和泛化能力。