Cycling stress assessment, which quantifies cyclists' perceived stress
imposed by the built environment and motor traffics, increasingly informs
cycling infrastructure planning and cycling route recommendation. However,
currently calculating cycling stress is slow and data-intensive, which hinders
its broader application. In this paper, We propose a deep learning framework to
support accurate, fast, and large-scale cycling stress assessments for urban
road networks based on street-view images. Our framework features i) a
contrastive learning approach that leverages the ordinal relationship among
cycling stress labels, and ii) a post-processing technique that enforces
spatial smoothness into our predictions. On a dataset of 39,153 road segments
collected in Toronto, Canada, our results demonstrate the effectiveness of our
deep learning framework and the value of using image data for cycling stress
assessment in the absence of high-quality road geometry and motor traffic data.

我们提出了一个基于深度学习框架的快速高效的大规模骑行压力评估方法，通过街景图像对城市道路网络进行评估，结果表明在缺乏高质量道路几何和交通数据的情况下，使用图像数据进行骑行压力评估是有效的。

AutoLTS: 利用对比学习和空间后处理自动化循环应力评估

AutoLTS: Automating Cycling Stress Assessment via Contrastive Learning  and Spatial Post-processing

In this paper, we evaluate the quality of knowledge representations encoded
in deep neural networks (DNNs) for 3D point cloud processing. We propose a
method to disentangle the overall model vulnerability into the sensitivity to
the rotation, the translation, the scale, and local 3D structures. Besides, we
also propose metrics to evaluate the spatial smoothness of encoding 3D
structures, and the representation complexity of the DNN. Based on such
analysis, experiments expose representation problems with classic DNNs, and
explain the utility of the adversarial training.

本文评估了编码在深度神经网络（DNNs）中的知识表征在 3D 点云处理方面的质量，并提出了一种将总体模型漏洞分解为旋转、平移、尺度和本地 3D 结构敏感性的方法，同时提出了评估编码 3D 结构的空间平滑性和 DNN 表征复杂度的度量方法。根据这样的分析，实验揭示了经典 DNN 的表征问题，并解释了对抗性训练的实用性。

深度神经网络在 3D 点云处理中的表示质量解释

Interpreting Representation Quality of DNNs for 3D Point Cloud  Processing

This paper proposes a simple spatial-temporal smoothness based method for
solving dense non-rigid structure-from-motion (NRSfM). First, we revisit the
temporal smoothness and demonstrate that it can be extended to dense case
directly. Second, we propose to exploit the spatial smoothness by resorting to
the Laplacian of the 3D non-rigid shape. Third, to handle real world noise and
outliers in measurements, we robustify the data term by using the $L_1$ norm.
In this way, our method could robustly exploit both spatial and temporal
smoothness effectively and make dense non-rigid reconstruction easy. Our method
is very easy to implement, which involves solving a series of least squares
problems. Experimental results on both synthetic and real image dense NRSfM
tasks show that the proposed method outperforms state-of-the-art dense
non-rigid reconstruction methods.

本文提出了一种基于时空平滑的简单非刚性运动稠密重构方法，通过同时利用时域平滑性和空间平滑性，以及使用拉普拉斯算子和 L1 范数来处理实际测量中的噪声和异常值，解决了非刚性结构运动重构难的问题，并且在合成和实际图像上的实验结果表明，该方法优于现有的非刚性重构方法。