Objects with complex structures pose significant challenges to existing
instance segmentation methods that rely on boundary or affinity maps, which are
vulnerable to small errors around contacting pixels that cause noticeable
connectivity change. While the distance transform (DT) makes instance interiors
and boundaries more distinguishable, it tends to overlook the intra-object
connectivity for instances with varying width and result in over-segmentation.
To address these challenges, we propose a skeleton-aware distance transform
(SDT) that combines the merits of object skeleton in preserving connectivity
and DT in modeling geometric arrangement to represent instances with arbitrary
structures. Comprehensive experiments on histopathology image segmentation
demonstrate that SDT achieves state-of-the-art performance.

提出了一种骨架感知的距离变换方法（SDT），其通过将对象骨架和距离变换相结合，以表示具有任意结构的实例，实验证明 SDT 在组织病理学图像分割中实现了最先进的性能。

骨架感知距离变换的结构保持实例分割

Structure-Preserving Instance Segmentation via Skeleton-Aware Distance  Transform

Neural Scene Flow Prior (NSFP) is of significant interest to the vision
community due to its inherent robustness to out-of-distribution (OOD) effects
and its ability to deal with dense lidar points. The approach utilizes a
coordinate neural network to estimate scene flow at runtime, without any
training. However, it is up to 100 times slower than current state-of-the-art
learning methods. In other applications such as image, video, and radiance
function reconstruction innovations in speeding up the runtime performance of
coordinate networks have centered upon architectural changes. In this paper, we
demonstrate that scene flow is different -- with the dominant computational
bottleneck stemming from the loss function itself (i.e., Chamfer distance).
Further, we rediscover the distance transform (DT) as an efficient,
correspondence-free loss function that dramatically speeds up the runtime
optimization. Our fast neural scene flow (FNSF) approach reports for the first
time real-time performance comparable to learning methods, without any training
or OOD bias on two of the largest open autonomous driving (AV) lidar datasets
Waymo Open and Argoverse.

本文提出了一种名为 “快速神经场景流”（FNSF）的方法，通过使用距离变换（DT）作为损失函数，使运行时间大大缩短，使其无需训练和偏见，从而实现了与当前最先进的学习方法相当的实时性能并成功在两个最大的自主驾驶（AV）激光雷达数据集 Waymo Open 和 Argoverse 上运行。

快速神经场景流

Fast Neural Scene Flow

Skeleton extraction is a task focused on providing a simple representation of
an object by extracting the skeleton from the given binary or RGB image. In
recent years many attractive works in skeleton extraction have been made. But
as far as we know, there is little research on how to utilize the context
information in the binary shape of objects. In this paper, we propose an
attention-based model called Context Attention Network (CANet), which
integrates the context extraction module in a UNet architecture and can
effectively improve the ability of network to extract the skeleton pixels.
Meanwhile, we also use some novel techniques including distance transform,
weight focal loss to achieve good results on the given dataset. Finally,
without model ensemble and with only 80% of the training images, our method
achieves 0.822 F1 score during the development phase and 0.8507 F1 score during
the final phase of the Pixel SkelNetOn Competition, ranking 1st place on the
leaderboard.

本文提出了一种基于上下文信息并采用 UNet 结构的注意力模型 CANet 来提取轮廓骨架像素，同时采用距离变换和加权重点损失等技术来取得优秀的结果，并在 Pixel SkelNetOn 竞赛中获得了第一名。

上下文注意力网络用于骨架提取

Context Attention Network for Skeleton Extraction

Understanding visual scenes relies more and more on dense pixel-wise
classification obtained via deep fully convolutional neural networks. However,
due to the nature of the networks, predictions often suffer from blurry
boundaries and ill-segmented shapes, fueling the need for post-processing. This
work introduces a new semantic segmentation regularization based on the
regression of a distance transform. After computing the distance transform on
the label masks, we train a FCN in a multi-task setting in both discrete and
continuous spaces by learning jointly classification and distance regression.
This requires almost no modification of the network structure and adds a very
low overhead to the training process. Learning to approximate the distance
transform back-propagates spatial cues that implicitly regularizes the
segmentation. We validate this technique with several architectures on various
datasets, and we show significant improvements compared to competitive
baselines.

通过在标签掩码上计算距离变换，训练一个全卷积神经网络来同时学习分类和距离回归，可以有效地应用于视觉场景中的语义分割并有效解决边界模糊和分割失真的问题。