This paper focuses on increasing the resolution of depth maps obtained from
3D cameras using structured light technology. Two deep learning models FDSR and
DKN are modified to work with high-resolution data, and data pre-processing
techniques are implemented for stable training. The models are trained on our
custom dataset of 1200 3D scans. The resulting high-resolution depth maps are
evaluated using qualitative and quantitative metrics. The approach for depth
map upsampling offers benefits such as reducing the processing time of a
pipeline by first downsampling a high-resolution depth map, performing various
processing steps at the lower resolution and upsampling the resulting depth map
or increasing the resolution of a point cloud captured in lower resolution by a
cheaper device. The experiments demonstrate that the FDSR model excels in terms
of faster processing time, making it a suitable choice for applications where
speed is crucial. On the other hand, the DKN model provides results with higher
precision, making it more suitable for applications that prioritize accuracy.

本文针对使用结构光技术获取的深度图进行提高分辨率的问题，通过修改 FDSR 和 DKN 两个深度学习模型以适应高分辨率数据，并实施数据预处理技术进行稳定训练。在我们自定义的 1200 个 3D 扫描数据集上对模型进行训练，并使用定性和定量评估指标对实现的高分辨率深度图进行评估。通过首先对高分辨率深度图进行下采样，对低分辨率数据进行各种处理步骤，然后上采样深度图或通过更便宜的设备提高低分辨率捕获的点云的分辨率，实现了深度图上采样的方法具有减少处理时间的好处。实验表明，FDSR 模型在处理时间上表现优异，适用于在速度至关重要的应用；而 DKN 模型提供了更高精度的结果，适合优先考虑准确性的应用。

使用深度学习的结构光扫描数据的超采样

Supersampling of Data from Structured-light Scanner with Deep Learning

Heart failure is a life-threatening condition that affects millions of people
worldwide. The ability to accurately predict patient survival can aid in early
intervention and improve patient outcomes. In this study, we explore the
potential of utilizing data pre-processing techniques and the Extra-Tree (ET)
feature selection method in conjunction with the Random Forest (RF) classifier
to improve survival prediction in heart failure patients. By leveraging the
strengths of ET feature selection, we aim to identify the most significant
predictors associated with heart failure survival. Using the public UCL Heart
failure (HF) survival dataset, we employ the ET feature selection algorithm to
identify the most informative features. These features are then used as input
for grid search of RF. Finally, the tuned RF Model was trained and evaluated
using different matrices. The approach was achieved 98.33% accuracy that is the
highest over the exiting work.

利用数据预处理技术和 Extra-Tree 特征选择方法结合 Random Forest 分类器，提高心衰患者生存预测准确性。采用 UCL 心衰生存数据集进行特征选择和调优，获得了 98.33% 的最高准确率。