Most recent 6D object pose estimation methods first use object detection to
obtain 2D bounding boxes before actually regressing the pose. However, the
general object detection methods they use are ill-suited to handle cluttered
scenes, thus producing poor initialization to the subsequent pose network. To
address this, we propose a rigidity-aware detection method exploiting the fact
that, in 6D pose estimation, the target objects are rigid. This lets us
introduce an approach to sampling positive object regions from the entire
visible object area during training, instead of naively drawing samples from
the bounding box center where the object might be occluded. As such, every
visible object part can contribute to the final bounding box prediction,
yielding better detection robustness. Key to the success of our approach is a
visibility map, which we propose to build using a minimum barrier distance
between every pixel in the bounding box and the box boundary. Our results on
seven challenging 6D pose estimation datasets evidence that our method
outperforms general detection frameworks by a large margin. Furthermore,
combined with a pose regression network, we obtain state-of-the-art pose
estimation results on the challenging BOP benchmark.

提出了一种基于 6D 目标姿态估计的刚性感知检测方法，利用可见目标区域进行训练样本的采样，使用最小障碍距离构建了可见性图，产生更好的检测性能，并结合姿态回归网络在很多 6D 目标姿态评估数据集中获得了最佳结果。

六自由度物体姿态估计刚性感知检测

Rigidity-Aware Detection for 6D Object Pose Estimation

In this paper, we propose a CNN-based framework for online MOT. This
framework utilizes the merits of single object trackers in adapting appearance
models and searching for target in the next frame. Simply applying single
object tracker for MOT will encounter the problem in computational efficiency
and drifted results caused by occlusion. Our framework achieves computational
efficiency by sharing features and using ROI-Pooling to obtain individual
features for each target. Some online learned target-specific CNN layers are
used for adapting the appearance model for each target. In the framework, we
introduce spatial-temporal attention mechanism (STAM) to handle the drift
caused by occlusion and interaction among targets. The visibility map of the
target is learned and used for inferring the spatial attention map. The spatial
attention map is then applied to weight the features. Besides, the occlusion
status can be estimated from the visibility map, which controls the online
updating process via weighted loss on training samples with different occlusion
statuses in different frames. It can be considered as temporal attention
mechanism. The proposed algorithm achieves 34.3% and 46.0% in MOTA on
challenging MOT15 and MOT16 benchmark dataset respectively.

本文提出了一种基于 CNN 的框架，用于在线多目标跟踪，并引入了空间 - 时间注意机制处理 Occlusion 问题，从而实现在 MOT15 和 MOT16 基准数据集上 34.3% 到 46.0% 的多目标跟踪性能。