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。

利用形状补全实现三维类脉冲跟踪

Leveraging Shape Completion for 3D Siamese Tracking

Most tracking-by-detection methods employ a local search window around the
predicted object location in the current frame assuming the previous location
is accurate, the trajectory is smooth, and the computational capacity permits a
search radius that can accommodate the maximum speed yet small enough to reduce
mismatches. These, however, may not be valid always, in particular for fast and
irregularly moving objects. Here, we present an object tracker that is not
limited to a local search window and has ability to probe efficiently the
entire frame. Our method generates a small number of "high-quality" proposals
by a novel instance-specific objectness measure and evaluates them against the
object model that can be adopted from an existing tracking-by-detection
approach as a core tracker. During the tracking process, we update the object
model concentrating on hard false-positives supplied by the proposals, which
help suppressing distractors caused by difficult background clutters, and learn
how to re-rank proposals according to the object model. Since we reduce
significantly the number of hypotheses the core tracker evaluates, we can use
richer object descriptors and stronger detector. Our method outperforms most
recent state-of-the-art trackers on popular tracking benchmarks, and provides
improved robustness for fast moving objects as well as for ultra low-frame-rate
videos.

本文提出了一种物体跟踪方法，通过实例特定的物体性度量产生 “高质量” 提议，并更新物体模型，以集中处理这些提议中的困难误报，并学习按对象模型对提议重新排名，在流畅跟踪移动对象和超低帧速率视频方面表现良好。