# Research on Panorama Generation from a Multi-Camera System by Object-Distance Estimation
## 4 Experimental Results
### Quantitative Analysis  
Our quantitative analysis was conducted based on the following steps:  
1. Each pixel within every overlapping region of a panoramic image was individually projected onto two overlapping original images captured by adjacent cameras, and the grayscale value of the projection points was obtained. In this way, two panoramic image patches were generated for each overlapping region, as shown in Figure 13.
2. The SIFT [34] feature points were then extracted and matched for each pair of panoramic image patches. The average Euclidean distance was then computed between corresponding feature points in every pair of panoramic image patches. In addition, the parameters of the Structural Similarity Index (SSIM) [31], Peak Signal-to-Noise Ratio (PSNR) [35], Normalized Cross-Correlation (NCC) coefficients, and Stitched Image Quality Evaluator (SIQE) [36] were determined to quantify the dissimilarities between each pair of panoramic image patches. The Root Mean Square Error (RMSE), average SSIM, average PSNR, and average NCC are further computed based on the average Euclidean distances, SSIM values, PSNR values, NCC coefficients, and SIQE respectively, obtained from the dataset of 160 panoramas.
3. Moreover, using the open-source library functions [37], we calculated the Stitched Panoramic Image Quality Assessment (SPIQA) [38] parameter for each group of images from eight cameras and the corresponding generated panoramic images. Then, we calculated the average of 160 SPIQAs.  
This comprehensive analysis allowed us to evaluate the quality and dissimilarities based on various metrics and parameters, encompassing both structural and pixel-wise differences, as depicted in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scene** & **Method** & **RMSE** & **SSIM** & **PSNR** & **NCC** & **SPIQA** & **SIQE** & **Time(s)** \\ \hline \multirow{3}{*}{Outdoor} & Method A & 6.3341 & 0.5530 & 16.2321 & 0.7076 & 0.6131 & 60.0379 & 8.9112 \\  & Method B & 8.2422 & 0.5575 & 15.5963 & 0.5650 & 0.5839 & 54.7586 & 0.1251  
Because the object distance in the outdoor scene is significantly larger than the eccentric distance between the optical center of a sub-camera and that of the virtual panoramic camera, the stitching accuracy of a panorama is less affected by inconsistencies in object-distance information within the surrounding scene. Thus, the proposed method only has a small improvement compared to Methods A and B. However, due to the restricted range of object-distances in indoor scenes, the eccentricity errors between cameras cannot be disregarded and may lead to substantial stitching errors when employing both Method A and Method B, especially when there are variations in the object-distance. Our proposed algorithm generates a panorama by utilizing the multiple projection radiuses from the estimation of object-distance information, which demonstrates far higher accuracy than Method A and B. In addition, it can be seen clearly that the stitching time of Method C is much higher than that of Method A and B.