We present MV2Cyl, a novel method for reconstructing 3D from 2D multi-view
images, not merely as a field or raw geometry but as a sketch-extrude CAD
model. Extracting extrusion cylinders from raw 3D geometry has been extensively
researched in computer vision, while the processing of 3D data through neural
networks has remained a bottleneck. Since 3D scans are generally accompanied by
multi-view images, leveraging 2D convolutional neural networks allows these
images to be exploited as a rich source for extracting extrusion cylinder
information. However, we observe that extracting only the surface information
of the extrudes and utilizing it results in suboptimal outcomes due to the
challenges in the occlusion and surface segmentation. By synergizing with the
extracted base curve information, we achieve the optimal reconstruction result
with the best accuracy in 2D sketch and extrude parameter estimation. Our
experiments, comparing our method with previous work that takes a raw 3D point
cloud as input, demonstrate the effectiveness of our approach by taking
advantage of multi-view images.

利用多视角图像，我们提出了一种名为 MV2Cyl 的新方法，用于从 2D 多视角图像中重建 3D 物体，不仅作为一个场或原始几何形状，而是作为一种草图提取的 CAD 模型。

MV2Cyl: 多视角图像重建三维挤压圆柱体

MV2Cyl: Reconstructing 3D Extrusion Cylinders from Multi-View Images

The segmentation of organs in volumetric medical images plays an important
role in computer-aided diagnosis and treatment/surgery planning. Conventional
2D convolutional neural networks (CNNs) can hardly exploit the spatial
correlation of volumetric data. Current 3D CNNs have the advantage to extract
more powerful volumetric representations but they usually suffer from occupying
excessive memory and computation nevertheless. In this study we aim to enhance
the 2D networks with contextual information for better volumetric image
segmentation. Accordingly, we propose a contextual embedding learning approach
to facilitate 2D CNNs capturing spatial information properly. Our approach
leverages the learned embedding and the slice-wisely neighboring matching as a
soft cue to guide the network. In such a way, the contextual information can be
transferred slice-by-slice thus boosting the volumetric representation of the
network. Experiments on challenging prostate MRI dataset (PROMISE12) and
abdominal CT dataset (CHAOS) show that our contextual embedding learning can
effectively leverage the inter-slice context and improve segmentation
performance. The proposed approach is a plug-and-play, and memory-efficient
solution to enhance the 2D networks for volumetric segmentation. The code will
be publicly available.

该研究旨在通过上下文嵌入学习方法增强二维卷积神经网络以更好地进行三维图像分割，结果表明该方法能够有效利用层间上下文信息并提高分割性能。

上下文嵌入学习用于增强三维图像分割的二维网络

Contextual Embedding Learning to Enhance 2D Networks for Volumetric  Image Segmentation

As a fundamental part of computational healthcare, Computer Tomography (CT)
and Magnetic Resonance Imaging (MRI) provide volumetric data, making the
development of algorithms for 3D image analysis a necessity. Despite being
computationally cheap, 2D Convolutional Neural Networks can only extract
spatial information. In contrast, 3D CNNs can extract three-dimensional
features, but they have higher computational costs and latency, which is a
limitation for clinical practice that requires fast and efficient models.
Inspired by the field of video action recognition we propose a new 2D-based
model dubbed Slice SHift UNet (SSH-UNet) which encodes three-dimensional
features at 2D CNN's complexity. More precisely multi-view features are
collaboratively learned by performing 2D convolutions along the three
orthogonal planes of a volume and imposing a weights-sharing mechanism. The
third dimension, which is neglected by the 2D convolution, is reincorporated by
shifting a portion of the feature maps along the slices' axis. The
effectiveness of our approach is validated in Multi-Modality Abdominal
Multi-Organ Segmentation (AMOS) and Multi-Atlas Labeling Beyond the Cranial
Vault (BTCV) datasets, showing that SSH-UNet is more efficient while on par in
performance with state-of-the-art architectures.

基于视频动作识别领域的灵感，我们提出了一种名为 Slice Shift UNet (SSH-UNet) 的新型二维模型，以 2D 卷积神经网络的复杂度编码三维特征，通过沿切片轴移动部分特征图重新融合被 2D 卷积忽视的第三个维度。在多模态腹部多器官分割（AMOS）和除颅骨外多图谱标记（BTCV）数据集中验证了我们方法的有效性，表明 SSH-UNet 在性能上与最先进的架构相媲美，且更高效。

时空建模遭遇 3D 医学图像分析：Slice-Shift UNet 与多视图融合

Spatiotemporal Modeling Encounters 3D Medical Image Analysis:  Slice-Shift UNet with Multi-View Fusion

In recent years, a number of approaches based on 2D or 3D convolutional
neural networks (CNN) have emerged for video action recognition, achieving
state-of-the-art results on several large-scale benchmark datasets. In this
paper, we carry out in-depth comparative analysis to better understand the
differences between these approaches and the progress made by them. To this
end, we develop an unified framework for both 2D-CNN and 3D-CNN action models,
which enables us to remove bells and whistles and provides a common ground for
fair comparison. We then conduct an effort towards a large-scale analysis
involving over 300 action recognition models. Our comprehensive analysis
reveals that a) a significant leap is made in efficiency for action
recognition, but not in accuracy; b) 2D-CNN and 3D-CNN models behave similarly
in terms of spatio-temporal representation abilities and transferability. Our
codes are available at this https URL

本文分析基于 2D 或 3D CNN 的视频动作识别方法，比较这些方法之间的区别和进展，并开发了一个统一框架进行公平比较，发现在动作识别方面取得了显著的提高，但准确性未有进展，探究了 2D-CNN 与 3D-CNN 模型在时空表示能力和可传递性方面的相似之处。