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

Visual re-localization means using a single image as input to estimate the
camera's location and orientation relative to a pre-recorded environment. The
highest-scoring methods are "structure based," and need the query camera's
intrinsics as an input to the model, with careful geometric optimization. When
intrinsics are absent, methods vie for accuracy by making various other
assumptions. This yields fairly good localization scores, but the models are
"narrow" in some way, eg., requiring costly test-time computations, or depth
sensors, or multiple query frames. In contrast, our proposed method makes few
special assumptions, and is fairly lightweight in training and testing.
Our pose regression network learns from only relative poses of training
scenes. For inference, it builds a graph connecting the query image to training
counterparts and uses a graph neural network (GNN) with image representations
on nodes and image-pair representations on edges. By efficiently passing
messages between them, both representation types are refined to produce a
consistent camera pose estimate. We validate the effectiveness of our approach
on both standard indoor (7-Scenes) and outdoor (Cambridge Landmarks) camera
re-localization benchmarks. Our relative pose regression method matches the
accuracy of absolute pose regression networks, while retaining the
relative-pose models' test-time speed and ability to generalize to non-training
scenes.

本研究提出了一种基于姿态回归网络和图神经网络相结合的视觉重新定位方法，可在没有内参信息的情况下达到与绝对姿态回归网络相当的准确性，同时保持相对姿态回归模型的高测试速度和对非训练场景的适用性。

使用图神经网络和相对姿态监督的视觉相机定位

Visual Camera Re-Localization Using Graph Neural Networks and Relative  Pose Supervision

We consider the problem of relative pose regression in visual relocalization.
Recently, several promising approaches have emerged in this area. We claim that
even though they demonstrate on the same datasets using the same split to train
and test, a faithful comparison between them was not available since on
currently used evaluation metric, some approaches might perform favorably,
while in reality performing worse. We reveal a tradeoff between accuracy and
the 3D volume of the regressed subspace. We believe that unlike other
relocalization approaches, in the case of relative pose regression, the
regressed subspace 3D volume is less dependent on the scene and more affect by
the method used to score the overlap, which determined how closely sampled
viewpoints are. We propose three new metrics to remedy the issue mentioned
above. The proposed metrics incorporate statistics about the regression
subspace volume. We also propose a new pose regression network that serves as a
new baseline for this task. We compare the performance of our trained model on
Microsoft 7-Scenes and Cambridge Landmarks datasets both with the standard
metrics and the newly proposed metrics and adjust the overlap score to reveal
the tradeoff between the subspace and performance. The results show that the
proposed metrics are more robust to different overlap threshold than the
conventional approaches. Finally, we show that our network generalizes well,
specifically, training on a single scene leads to little loss of performance on
the other scenes.

我们提出了相对位姿回归的几种新度量方式并给出了一种新的姿态回归网络，将其与传统方法比较，结果表明新的度量方法比传统方法更稳健，并且我们的网络在单个场景训练模型后，在其他场景上的性能损失很小。