We propose a single-shot approach to determining 6-DoF pose of an object with
available 3D computer-aided design (CAD) model from a single RGB image. Our
method, dubbed MRC-Net, comprises two stages. The first performs pose
classification and renders the 3D object in the classified pose. The second
stage performs regression to predict fine-grained residual pose within class.
Connecting the two stages is a novel multi-scale residual correlation (MRC)
layer that captures high-and-low level correspondences between the input image
and rendering from first stage. MRC-Net employs a Siamese network with shared
weights between both stages to learn embeddings for input and rendered images.
To mitigate ambiguity when predicting discrete pose class labels on symmetric
objects, we use soft probabilistic labels to define pose class in the first
stage. We demonstrate state-of-the-art accuracy, outperforming all competing
RGB-based methods on four challenging BOP benchmark datasets: T-LESS, LM-O,
YCB-V, and ITODD. Our method is non-iterative and requires no complex
post-processing.

我们提出了一种单次拍摄方法，可以从一张 RGB 图像中确定具有 3D CAD 模型的物体的 6 自由度姿态。我们的方法称为 MRC-Net，包括两个阶段，利用 MRC 层获得输入图像和第一阶段渲染的高低级对应关系。MRC-Net 采用具有共享权重的孪生网络学习输入和渲染图像的嵌入表示。我们的方法在四个具有挑战性的 BOP 基准数据集上表现出色，超过了所有竞争的基于 RGB 的方法。我们的方法非迭代且不需要复杂的后处理。

MRC-Net: 多尺度残差相关的 6 自由度姿态估计

MRC-Net: 6-DoF Pose Estimation with MultiScale Residual Correlation

In contrast to the fully supervised methods using pixel-wise mask labels,
box-supervised instance segmentation takes advantage of the simple box
annotations, which has recently attracted a lot of research attentions. In this
paper, we propose a novel single-shot box-supervised instance segmentation
approach, which integrates the classical level set model with deep neural
network delicately. Specifically, our proposed method iteratively learns a
series of level sets through a continuous Chan-Vese energy-based function in an
end-to-end fashion. A simple mask supervised SOLOv2 model is adapted to predict
the instance-aware mask map as the level set for each instance. Both the input
image and its deep features are employed as the input data to evolve the level
set curves, where a box projection function is employed to obtain the initial
boundary. By minimizing the fully differentiable energy function, the level set
for each instance is iteratively optimized within its corresponding bounding
box annotation. The experimental results on four challenging benchmarks
demonstrate the leading performance of our proposed approach to robust instance
segmentation in various scenarios. The code is available at:
this https URL

该研究提出了一种新的单次盒监督实例分割方法，该方法将经典的水平集模型与深度神经网络结合到一起，提出了一种连续的 Chan-Vese 能量功能，采用简单的盒标注来学习一系列水平集，通过盒投影功能来获得初始边界，并在相应的边界框注释内逐步优化每个实例的水平集。实验结果表明该方法在各种情况下实现了鲁棒的实例分割效果。

基于框标注的水平集演化实例分割

Box-supervised Instance Segmentation with Level Set Evolution

We propose a single-shot approach for simultaneously detecting an object in
an RGB image and predicting its 6D pose without requiring multiple stages or
having to examine multiple hypotheses. Unlike a recently proposed single-shot
technique for this task (Kehl et al., ICCV'17) that only predicts an
approximate 6D pose that must then be refined, ours is accurate enough not to
require additional post-processing. As a result, it is much faster - 50 fps on
a Titan X (Pascal) GPU - and more suitable for real-time processing. The key
component of our method is a new CNN architecture inspired by the YOLO network
design that directly predicts the 2D image locations of the projected vertices
of the object's 3D bounding box. The object's 6D pose is then estimated using a
PnP algorithm.
For single object and multiple object pose estimation on the LINEMOD and
OCCLUSION datasets, our approach substantially outperforms other recent
CNN-based approaches when they are all used without post-processing. During
post-processing, a pose refinement step can be used to boost the accuracy of
the existing methods, but at 10 fps or less, they are much slower than our
method.

该论文提出了一种单次操作的方法，可以在不需要多个阶段或检查多个假设的同时，在 RGB 图像中检测对象并预测其 6D 姿态。通过采用新的 CNN 架构和 PnP 算法，该方法可以以 50fps 的速度在 Titan X GPU 上运行，比其他最近的 CNN 方法具有更高的准确性和更适合实时处理。