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 perception can provide crucial information for
urban navigation in challenging light or weather conditions. In this paper, we
investigate the versatility of Shape Completion for 3D Object Tracking in LIDAR
point clouds. We design a Siamese tracker that encodes model and candidate
shapes into a compact latent representation. We regularize the encoding by
enforcing the latent representation to decode into an object model shape. We
observe that 3D object tracking and 3D shape completion complement each other.
Learning a more meaningful latent representation shows better discriminatory
capabilities, leading to improved tracking performance. We test our method on
the KITTI Tracking set using car 3D bounding boxes. Our model reaches a 76.94%
Success rate and 81.38% Precision for 3D Object Tracking, with the shape
completion regularization leading to an improvement of 3% in both metrics.

本文探讨了形状完成技术在 LIDAR 三维物体追踪中的应用，设计了一个 Siamese 追踪器，通过对模型和候选形状进行编码，实现了更好的 3D 物体追踪。在 KITTI 追踪数据上的实验结果表明，该模型将成功率和准确率均提升了 3％以上。