In this work, we present a novel dense-correspondence method for 6DoF object
pose estimation from a single RGB-D image. While many existing data-driven
methods achieve impressive performance, they tend to be time-consuming due to
their reliance on rendering-based refinement approaches. To circumvent this
limitation, we present HiPose, which establishes 3D-3D correspondences in a
coarse-to-fine manner with a hierarchical binary surface encoding. Unlike
previous dense-correspondence methods, we estimate the correspondence surface
by employing point-to-surface matching and iteratively constricting the surface
until it becomes a correspondence point while gradually removing outliers.
Extensive experiments on public benchmarks LM-O, YCB-V, and T-Less demonstrate
that our method surpasses all refinement-free methods and is even on par with
expensive refinement-based approaches. Crucially, our approach is
computationally efficient and enables real-time critical applications with high
accuracy requirements. Code and models will be released.

本研究提出了一种新的密集对应方法，用于从单个 RGB-D 图像估计对象的六自由度姿态。与现有的数据驱动方法相比，该方法通过使用分层二进制曲面编码以粗略到精细的方式建立 3D-3D 对应关系，并在表面上采用点到曲面匹配和迭代地收缩曲面直至成为对应点，逐渐去除异常值。在公共基准测试集 LM-O、YCB-V 和 T-Less 上进行了大量实验，结果显示我们的方法超越了所有无精炼方法，并且与高成本的精炼方法相媲美。关键是，我们的方法计算效率高，可以在具有高准确性要求的实时关键应用中使用。代码和模型将被发布。

HiPose: RGB-D 6DoF 物体姿态估计的分层二进制表面编码与对应关系剪枝

HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning  for RGB-D 6DoF Object Pose Estimation

Establishing correspondences from image to 3D has been a key task of 6DoF
object pose estimation for a long time. To predict pose more accurately, deeply
learned dense maps replaced sparse templates. Dense methods also improved pose
estimation in the presence of occlusion. More recently researchers have shown
improvements by learning object fragments as segmentation. In this work, we
present a discrete descriptor, which can represent the object surface densely.
By incorporating a hierarchical binary grouping, we can encode the object
surface very efficiently. Moreover, we propose a coarse to fine training
strategy, which enables fine-grained correspondence prediction. Finally, by
matching predicted codes with object surface and using a PnP solver, we
estimate the 6DoF pose. Results on the public LM-O and YCB-V datasets show
major improvement over the state of the art w.r.t. ADD(-S) metric, even
surpassing RGB-D based methods in some cases.

本文提出了一种基于离散描述符的 6DoF 物体姿态估计方法，通过学习物体表面密集编码和粗到细的训练策略，实现更精确的物体姿态估计，并在公共数据集上取得了超过现有方法的结果。

ZebraPose: 6DoF 物体姿态估计的粗细表面编码

ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose  Estimation

In this work, we present a novel data-driven method for robust 6DoF object
pose estimation from a single RGBD image. Unlike previous methods that directly
regressing pose parameters, we tackle this challenging task with a
keypoint-based approach. Specifically, we propose a deep Hough voting network
to detect 3D keypoints of objects and then estimate the 6D pose parameters
within a least-squares fitting manner. Our method is a natural extension of
2D-keypoint approaches that successfully work on RGB based 6DoF estimation. It
allows us to fully utilize the geometric constraint of rigid objects with the
extra depth information and is easy for a network to learn and optimize.
Extensive experiments were conducted to demonstrate the effectiveness of
3D-keypoint detection in the 6D pose estimation task. Experimental results also
show our method outperforms the state-of-the-art methods by large margins on
several benchmarks. Code and video are available at
this https URL

本文提出了一种基于数据驱动的方法，使用关键点检测和 Hough 投票网络，实现了从单个 RGBD 图像稳健地估算 6DoF 物体姿态，并在多个基准测试中表现出色。