Estimating the pose of an unseen object is the goal of the challenging
one-shot pose estimation task. Previous methods have heavily relied on feature
matching with great success. However, these methods are often inefficient and
limited by their reliance on pre-trained models that have not be designed
specifically for pose estimation. In this paper we propose PoseMatcher, an
accurate model free one-shot object pose estimator that overcomes these
limitations. We create a new training pipeline for object to image matching
based on a three-view system: a query with a positive and negative templates.
This simple yet effective approach emulates test time scenarios by cheaply
constructing an approximation of the full object point cloud during training.
To enable PoseMatcher to attend to distinct input modalities, an image and a
pointcloud, we introduce IO-Layer, a new attention layer that efficiently
accommodates self and cross attention between the inputs. Moreover, we propose
a pruning strategy where we iteratively remove redundant regions of the target
object to further reduce the complexity and noise of the network while
maintaining accuracy. Finally we redesign commonly used pose refinement
strategies, zoom and 2D offset refinements, and adapt them to the one-shot
paradigm. We outperform all prior one-shot pose estimation methods on the
Linemod and YCB-V datasets as well achieve results rivaling recent
instance-level methods. The source code and models are available at
this https URL

本文提出了一种基于 three-view 系统的 PoseMatcher 模型来进行精确的无模型一次性物体位姿估计，同时引入了 IO-Layer 进行有效信息的交叉注意力，还设计了对象点云下的基于数字图像处理的目标选取和定位算法。实验结果证明该方法在 Linemod 和 YCB-V 数据集上表现优异。

通过深度特征匹配进行一次性 6D 物体姿态估计的 PoseMatcher

PoseMatcher: One-shot 6D Object Pose Estimation by Deep Feature Matching

One-shot pose estimation for tasks such as body joint localization, camera
pose estimation, and object tracking are generally noisy, and temporal filters
have been extensively used for regularization. One of the most widely-used
methods is the Kalman filter, which is both extremely simple and general.
However, Kalman filters require a motion model and measurement model to be
specified a priori, which burdens the modeler and simultaneously demands that
we use explicit models that are often only crude approximations of reality. For
example, in the pose-estimation tasks mentioned above, it is common to use
motion models that assume constant velocity or constant acceleration, and we
believe that these simplified representations are severely inhibitive. In this
work, we propose to instead learn rich, dynamic representations of the motion
and noise models. In particular, we propose learning these models from data
using long short term memory, which allows representations that depend on all
previous observations and all previous states. We evaluate our method using
three of the most popular pose estimation tasks in computer vision, and in all
cases we obtain state-of-the-art performance.

本研究提出了一种基于长短时记忆学习（long short term memory）的方法来丰富动态运动和噪声模型的表征，以应用于计算机视觉中的三个最受欢迎的姿态估计任务，并在所有情况下获得最先进的性能表现。