We address the task of 6D multi-object pose: given a set of known 3D objects
and an RGB or RGB-D input image, we detect and estimate the 6D pose of each
object. We propose a new approach to 6D object pose estimation which consists
of an end-to-end differentiable architecture that makes use of geometric
knowledge. Our approach iteratively refines both pose and correspondence in a
tightly coupled manner, allowing us to dynamically remove outliers to improve
accuracy. We use a novel differentiable layer to perform pose refinement by
solving an optimization problem we refer to as Bidirectional Depth-Augmented
Perspective-N-Point (BD-PnP). Our method achieves state-of-the-art accuracy on
standard 6D Object Pose benchmarks. Code is available at
this https URL

提出了一种基于几何知识的端到端可微建构的方法，通过 BD-PnP 优化问题迭代地细化姿态估计和对应关系，并动态地删除离群值以提高准确性，从而在标准的 6D 物体姿态基准测试上实现了最新的准确性。

6D 多目标姿态估计的耦合迭代优化

Coupled Iterative Refinement for 6D Multi-Object Pose Estimation

We propose a method for converting a single RGB-D input image into a 3D photo
- a multi-layer representation for novel view synthesis that contains
hallucinated color and depth structures in regions occluded in the original
view. We use a Layered Depth Image with explicit pixel connectivity as
underlying representation, and present a learning-based inpainting model that
synthesizes new local color-and-depth content into the occluded region in a
spatial context-aware manner. The resulting 3D photos can be efficiently
rendered with motion parallax using standard graphics engines. We validate the
effectiveness of our method on a wide range of challenging everyday scenes and
show fewer artifacts compared with the state of the arts.

提出一种将单个 RGB-D 输入图像转换成 3D 照片的方法，其中包含了多层的新视角综合结构，可以补全原始视角中被遮挡区域的缺失的颜色和深度信息，并经验证在各种现实场景中比现有技术实现更少的图像伪影。

上下文感知分层深度修复的三维摄影

3D Photography using Context-aware Layered Depth Inpainting

We propose a benchmark for 6D pose estimation of a rigid object from a single
RGB-D input image. The training data consists of a texture-mapped 3D object
model or images of the object in known 6D poses. The benchmark comprises of: i)
eight datasets in a unified format that cover different practical scenarios,
including two new datasets focusing on varying lighting conditions, ii) an
evaluation methodology with a pose-error function that deals with pose
ambiguities, iii) a comprehensive evaluation of 15 diverse recent methods that
captures the status quo of the field, and iv) an online evaluation system that
is open for continuous submission of new results. The evaluation shows that
methods based on point-pair features currently perform best, outperforming
template matching methods, learning-based methods and methods based on 3D local
features. The project website is available at bop.felk.cvut.cz.

提出适用于单个 RGB-D 输入图像的刚体物体 6D 姿态估计基准，并通过姿态误差函数对姿态歧义进行处理，在 15 种不同方法的全面评估中发现，基于点对特征的方法目前表现最佳。