Object pose refinement is essential for robust object pose estimation.
Previous work has made significant progress towards instance-level object pose
refinement. Yet, category-level pose refinement is a more challenging problem
due to large shape variations within a category and the discrepancies between
the target object and the shape prior. To address these challenges, we
introduce a novel architecture for category-level object pose refinement. Our
approach integrates an HS-layer and learnable affine transformations, which
aims to enhance the extraction and alignment of geometric information.
Additionally, we introduce a cross-cloud transformation mechanism that
efficiently merges diverse data sources. Finally, we push the limits of our
model by incorporating the shape prior information for translation and size
error prediction. We conducted extensive experiments to demonstrate the
effectiveness of the proposed framework. Through extensive quantitative
experiments, we demonstrate significant improvement over the baseline method by
a large margin across all metrics.

本研究提出了一种新的框架用于类别级目标姿态细化，通过整合 HS 层和可学习仿射变换来增强几何信息的提取和对齐，同时引入了跨云变换机制来高效合并多样化数据源，并在翻译和尺寸误差预测中应用形状先验信息，通过大量定量实验证明了所提出框架在所有评测指标上相对于基线方法的显著提高。

GeoReF: 类别级别物体位姿细化的形状变化间的几何对齐

GeoReF: Geometric Alignment Across Shape Variation for Category-level  Object Pose Refinement

Recently, weakly-supervised image segmentation using weak annotations like
scribbles has gained great attention in computer vision and medical image
analysis, since such annotations are much easier to obtain compared to
time-consuming and labor-intensive labeling at the pixel/voxel level. However,
due to a lack of structure supervision on regions of interest (ROIs), existing
scribble-based methods suffer from poor boundary localization. Furthermore,
most current methods are designed for 2D image segmentation, which do not fully
leverage the volumetric information if directly applied to each image slice. In
this paper, we propose a scribble-based volumetric image segmentation,
Scribble2D5, which tackles 3D anisotropic image segmentation and aims to its
improve boundary prediction. To achieve this, we augment a 2.5D attention UNet
with a proposed label propagation module to extend semantic information from
scribbles and use a combination of static and active boundary prediction to
learn ROI's boundary and regularize its shape. Also, we propose an optional
add-on component, which incorporates the shape prior information from unpaired
segmentation masks to further improve model accuracy. Extensive experiments on
three public datasets and one private dataset demonstrate our Scribble2D5
achieves state-of-the-art performance on volumetric image segmentation using
scribbles and shape prior if available.

本文提出了 Scribble2D5，一种基于涂鸦的体积图像分割方法，旨在改善 3D 异向图像分割和边界预测。通过引入标签传播模块，扩展了涂鸦的语义信息，并使用静态和主动边界预测的组合学习感兴趣区域的边界并规范其形状，与此同时，通过融合未配对分割掩码的形状先验信息进一步提高模型的准确性。通过在三个公开数据集和一个私有数据集上的广泛实验，证明了 Scribble2D5 在使用涂鸦和形状先验数据进行体积图像分割方面取得了最先进的性能。

通过草图标注和形状先验进行体积医学图像分割

Volumetric Medical Image Segmentation via Scribble Annotations and Shape  Priors

Domain generalization typically requires data from multiple source domains
for model learning. However, such strong assumption may not always hold in
practice, especially in medical field where the data sharing is highly
concerned and sometimes prohibitive due to privacy issue. This paper studies
the important yet challenging single domain generalization problem, in which a
model is learned under the worst-case scenario with only one source domain to
directly generalize to different unseen target domains. We present a novel
approach to address this problem in medical image segmentation, which extracts
and integrates the semantic shape prior information of segmentation that are
invariant across domains and can be well-captured even from single domain data
to facilitate segmentation under distribution shifts. Besides, a test-time
adaptation strategy with dual-consistency regularization is further devised to
promote dynamic incorporation of these shape priors under each unseen domain to
improve model generalizability. Extensive experiments on two medical image
segmentation tasks demonstrate the consistent improvements of our method across
various unseen domains, as well as its superiority over state-of-the-art
approaches in addressing domain generalization under the worst-case scenario.

本文研究单一领域通用的医学图像分割问题，提出了一种利用领域间不变的形状先验信息和测试时的适应策略以提高模型通用性的方法，并在两个医学图像分割任务上进行了广泛实验验证。