Most recent 6D object pose methods use 2D optical flow to refine their
results. However, the general optical flow methods typically do not consider
the target's 3D shape information during matching, making them less effective
in 6D object pose estimation. In this work, we propose a shape-constraint
recurrent matching framework for 6D object pose estimation. We first compute a
pose-induced flow based on the displacement of 2D reprojection between the
initial pose and the currently estimated pose, which embeds the target's 3D
shape implicitly. Then we use this pose-induced flow to construct the
correlation map for the following matching iterations, which reduces the
matching space significantly and is much easier to learn. Furthermore, we use
networks to learn the object pose based on the current estimated flow, which
facilitates the computation of the pose-induced flow for the next iteration and
yields an end-to-end system for object pose. Finally, we optimize the optical
flow and object pose simultaneously in a recurrent manner. We evaluate our
method on three challenging 6D object pose datasets and show that it
outperforms the state of the art significantly in both accuracy and efficiency.

我们提出了一种基于形状约束的循环匹配框架，利用姿态引起的流来构造相关图，并使用网络来学习物体姿态，优化光流和物体姿态，取得了比现有技术显著更高的准确性和效率。

6D 物体姿态估计的形状约束递归流

Shape-Constraint Recurrent Flow for 6D Object Pose Estimation

We study the problem of response selection for multi-turn conversation in
retrieval-based chatbots. The task requires matching a response candidate with
a conversation context, whose challenges include how to recognize important
parts of the context, and how to model the relationships among utterances in
the context. Existing matching methods may lose important information in
contexts as we can interpret them with a unified framework in which contexts
are transformed to fixed-length vectors without any interaction with responses
before matching. The analysis motivates us to propose a new matching framework
that can sufficiently carry the important information in contexts to matching
and model the relationships among utterances at the same time. The new
framework, which we call a sequential matching framework (SMF), lets each
utterance in a context interacts with a response candidate at the first step
and transforms the pair to a matching vector. The matching vectors are then
accumulated following the order of the utterances in the context with a
recurrent neural network (RNN) which models the relationships among the
utterances. The context-response matching is finally calculated with the hidden
states of the RNN. Under SMF, we propose a sequential convolutional network and
sequential attention network and conduct experiments on two public data sets to
test their performance. Experimental results show that both models can
significantly outperform the state-of-the-art matching methods. We also show
that the models are interpretable with visualizations that provide us insights
on how they capture and leverage the important information in contexts for
matching.

我们提出了一种名为序列匹配框架（SMF）的新型匹配框架，该框架可以充分携带上下文中的重要信息，同时建模上下文中话语之间的关系，通过序列卷积网络和序列注意力网络实现，实验结果表明，这两种模型都可以显著优于现有技术，而可视化结果揭示了模型如何捕捉并利用上下文中的重要信息实现匹配。