# CAPDepth: 360 Monocular Depth Estimation by Content-Aware Projection
## 4 Experiments
### Datasets, Metrics, and Implementation Details  
We experimentally evaluate CAPDepth on three benchmark datasets: Stanford2D3D [36], Matterport3D [37], and 3D60 [3]. They are the most commonly used datasets for 360-degree panoramic depth estimation (such as PanoFormer [22], HRDfusion [17], OmniFusion [16], GLPanoDepth [21], etc., which have been used as experimental datasets in recent years), covering different types of scenes and data formats, and providing extensive and high-quality training and test samples. Matterport3D, a real-world dataset, was collected using the Matterport Pro 3D camera (Matterport, Inc., Sunnyvale, CA, USA), which contained 10,800 indoor panoramic images. Stanford2D3D and Matterport3D were collected in the same way, with a total of 1413 real panoramic images. Due to noise and missing data from the ToF sensor during collection, the ground-truth depth maps in these two datasets were inaccurate in some areas. In our experiments, we adjust the resolution to 512 \(\times\) 1024. Moreover, 3D60 includes the Stanford2D3D and Matterport3D datasets, along with the synthetic datasets SunCG [38] and SceneNet [39], resulting in a total of 35,977 panoramic images. This research uses the official training and testing splits, with a resolution set at 256 \(\times\) 512.  
To evaluate performance, this research uses the following evaluation metrics, which are also the most commonly used evaluation metrics in the field [3; 16; 17]: (1) Absolute relative error (Abs Rel), which is used to measure the relative error between the predicted depth value and the true depth value. It has a mild penalty for large errors and is more sensitive to small depth values. (2) Square relative error (Sq Rel), which is used to measure the square of the relative difference between the predicted depth value and the true depth value. It has a heavier penalty for large errors and emphasizes accuracy. (3) Root mean square error (RMSE), which provides an error measure consistent with the original depth unit and more clearly expresses the magnitude of the error. (4) Logarithmic space root mean square error (RMSE(log)), which calculates the root mean square error in logarithmic space, is more sensitive to the relative error of the depth value and can balance large-scale errors and small-scale errors. (5) The percentage of the ratio of the estimated depth to the ground truth depth that is less than the threshold \(\delta_{t}\), where \(t\in\{1.25,1.25^{2},1.25^{3}\}\), gives a sufficient fault tolerance evaluation.  
This research implements CAPDepth using PyTorch and trains it on two NVIDIA V100 GPUs. According to [16], this research also chooses the pretrained ResNet-34 [40] as the encoder based on the balance between performance and efficiency. Applying the adaptive and dynamic adjustment strategy, we use the Adam optimizer [41] and a cosine annealing learning-rate schedule [42] and set the initial learning rate to \(10\times 10^{-2}\), which is a widely used initial setting. The default number of patches is \(N\) = 18, the batch size is 16, and 80 epochs are trained on the three datasets. We will give the ablation experiments on parameter selection in Section 4.3. We also use horizontal translation and horizontal flipping for data augmentation.