## 4 Experiment Results and Discussion
### Discussion about the Effects of Camera Sensor Type on ED\({}^{2}\)IF\({}^{2}\)-Net  
In the previous experiments and applications, the images utilized were acquired using a standard camera sensor model, which allowed for capturing images without significant distortion. However, in various industries such as drone aerial photography, security surveillance, and automotive, wide-angle and fisheye imaging sensors are extensively employed. These sensors typically have a field of view (FOV) greater than 100 degrees, which is considerably larger compared to standard camera sensors. Hence, in this section, we primarily focus on discussing the effects of images captured by wide-angle and fisheye imaging sensors on the performance of ED\({}^{2}\)IF\({}^{2}\)-Net.  
There are existing works [62; 63; 64] that utilize images captured by wide-angle or fisheye sensors for 3D reconstruction and other related tasks. For instance, [PERSON] et al. [62] proposed a specific model for fisheye sensors and introduced sparse and dense multi-view 3D reconstruction methods based on this model. [PERSON] et al. [63] performed 3D reconstruction using images captured by fisheye sensors and standard lens models, respectively, employing the Pix4 Dmapper software. [PERSON] et al. [64] proposed a self-calibration method for wide-angle and fisheye cameras to correct the captured images, allowing for their utilization in 3D reconstruction and other tasks.  
In general, wide-angle sensors can capture images with a larger field of view compared to standard lenses, but they often introduce perspective distortion. This distortion can alter the shape of objects in the image, making it challenging for ED\({}^{2}\)IF\({}^{2}\)-Net trained on images acquired from the standard lens model to reconstruct high-fidelity object shapes. On the other hand, fisheye camera sensors can capture images with an extremely wide field of view but introduce barrel distortion, which causes even more severe distortion of objects in the image. Consequently, ED\({}^{2}\)IF\({}^{2}\)-Net faces difficulties in reconstructing accurate object shapes from such distorted images.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & **IoU\(\uparrow\)** & **CD\(\downarrow\)** & **EMD\(\downarrow\)** & **ECD-3D\(\downarrow\)** & **ECD-2D\(\downarrow\)** \\ \hline Surface & D\({}^{2}\)IM-Net & 51.4 & 6.87 & 3.01 & 5.86 & 2.25 \\ Detail & ED\({}^{2}\)IF\({}^{2}\)-Net-T & 52.3 & 6.69 & _2.88_ & 5.72 & _2.01_ \\ Transfer & ED\({}^{2}\)IF\({}^{2}\)-Net-L & **53.1** & **6.58** & **2.75** & **5.65** & **1.92** \\ \hline Pasting & D\({}^{2}\)IM-Net & 53.4 & 6.67 & 2.93 & 5.66 & 2.08 \\ a Logo & ED\({}^{2}\)IF\({}^{2}\)-Net-T & _54.6_ & 6.52 & _2.84_ & 5.57 & _1.88_ \\  & ED\({}^{2}\)IF\({}^{2}\)-Net-L & **55.8** & **6.36** & **2.69** & **5.42** & **1.75** \\ \hline \end{tabular}
\end{table}
Table 4: Quantitative results of D\({}^{2}\)IM-Net and ED\({}^{2}\)IF\({}^{2}\)-Net for various applications. Evaluation metrics also include IoU (%), CD (\(\times\)0.001), EMD (\(\times\)100), ECD-3D (\(\times\)0.01), and ECD-2D. The best results for each application are highlighted in **bold** and underlined, while the _italic_ one is the second.  
Figure 10: Examples about pasting a logo using ED\({}^{2}\)IF\({}^{2}\)-Net.  
To mitigate the effects of perspective distortion and barrel distortion caused by wide-angle and fisheye camera sensors on ED\({}^{2}\)IF\({}^{2}\)-Net, pre-processing techniques such as camera calibration [64] and image correction [65] can be employed to reduce the degree of image distortion. Another approach is to consider training ED\({}^{2}\)IF\({}^{2}\)-Net on publicly available datasets of images captured by wide-angle sensor models and fisheye sensor models, enabling the network to learn about the different distortions using its powerful feature extraction and learning capabilities.