## 2 Deep Learning for Depth Estimation  
The depth information of the surrounding is useful to understand the 3D scene. The traditional approach to provide depth information is to use 3D (depth) cameras that provide depth information. However, nowadays, commercially available 3D (depth) cameras have some disadvantages as mentioned before. Therefore, this has led us to search for and explore depth estimation possibilities with 2D (RGB) cameras. To select the appropriate model for our application, some considerations must be made, such as the loss function of deep learning models, which can have a significant impact on the training speed and overall performance of depth estimation [13]. Moreover, fast computation of the image is also crucial for the applicability of the methods, since in our final application with embedded systems we have to deal with a large amount of daily recording data for each patient. In addition, the robustness of the method to different conditions, such as light is important since the environment is not controlled during the acquisition. Therefore, a flexible loss function was proposed based on recent literature and the robustness and generality of the models were evaluated by zero-shot cross-dataset transfer [22]. Furthermore, providing high quality depth maps that more accurately capture object boundaries plays an important role in selecting the model for depth estimation. Therefore, two current candidates were applied to our dataset to verify their compatibility and test their robustness. The first candidate is a model whose robustness and generality was evaluated using zero-shot cross-dataset transfer (MiDaS), i.e., authors evaluated datasets that were not seen during training. The experiments confirm that mixing data from complementary sources significantly improves monocular depth estimation. They intentionally merged datasets with different properties and biases to scale depth in different environments and guide it through deep networks. [PERSON] et al. have presented a flexible loss function and a principled strategy for mixing datasets [22]. They also have shown that training models for monocular depth estimation on different datasets is challenging due to the differences in ground truth. Therefore, they needed to develop a loss function that is flexible enough to work with different data sources. Hence, they compared different loss functions and their results and evaluate the impact of encoder architectures such as ResNet-50 encoder, ResNet-101, ResNeXt-101 and so on. Finally, they proposed a flexible loss function and a principled shuffling strategy for monocular depth estimation. Figure 1 shows the network architecture proposed by [PERSON] et al. [23] used in the MiDaS method as their base architecture. Their network is based on a feed-forward ResNet [22]. To obtain more accurate predictions, they used a progressive refinement strategy to fuse multi-scale features. They used a residual convolution module and up-sampling to fuse the features. At the end, there is an adaptive convolution module that adjusts the channels of the feature maps and provides the final output. [PERSON] et al. used [PERSON]. [PERSON] et al. ResNet-based and multi-scale architecture to predict single-image depth. For the encoder part they initialized it with the pretrained ImageNet weights, and for other layers they initialized weights randomly using Adam. Figure 2 shows the result of depth estimation based on the MiDaS method for a single RGB image from our dataset. The second method we chose is the work of [PERSON] et al., which proposes a convolutional neural network to compute a high-resolution depth map for a single RGB image using transfer learning [13]. They have shown that the standard encoder-decoder method shows in Figure 3 can extract a high-resolution depth map that outperforms more complex training algorithms in terms of accuracy and visual quality. We chose this method since it is computationally cheap. They use a straightforward encoder-decoder architecture.