## 4 Omnidirectional Images
### Datasets for Omnidirectional Images  
Table 2 presents a synthetic overview describing the adoption of different datasets by different methods for visual saliency estimation in omnidirectional images. The most frequently adopted dataset is the one published with the Salient360! challenge [41], in some cases based on an old version of the same set [40]. The iSUN dataset [77] (interactive Scene UNderstanding) was used by [PERSON] et al. [35] to pre-train their solution, but does not involve omnidirectional images. The MIT dataset [78] from Massachusetts Institute of Technology was adopted for evaluation by [PERSON] et al. [73], but does not contain saliency ground truth information. A detailed description of all the relevant datasets is consequently presented in Table 3. These can be differentiated first and foremost by the stimuli characteristics, ranging from image resolution (when stored in equirectangular format), to duration of the exposition to the stimulus itself. The display device is typically either a head-mounted display such as Oculus Discovery Kit 2 (DK2), or a classical computer screen. In the latter case, the image is visualized in Normal Field of View, allowing the user to navigate the whole panorama with the use of mouse and keyboard. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Method** & \begin{tabular}{c} **[PERSON]** \\ **2019 [29]** \\ \end{tabular} & \begin{tabular}{c} **[PERSON]** \\ **2018 [33]** \\ \end{tabular} & \begin{tabular}{c} **[PERSON]** \\ **2018 [61]** \\ \end{tabular} & \begin{tabular}{c} **[PERSON]** \\ **2018 [62]** \\ \end{tabular} & \begin{tabular}{c} **[PERSON]** \\ **2018 [63]** \\ \end{tabular} & \begin{tabular}{c} **[PERSON]** \\ **2018 [36]** \\ \end{tabular} & \begin{tabular}{c} **[PERSON]** \\ **2018 [64]** \\ \end{tabular} & \begin{tabular}{c} **[PERSON]** \\ **2017 [34]** \\ \end{tabular} & \
\begin{tabular}{c} **[PERSON]** \\ **2017 [38]** \\ \end{tabular} &
\begin{tabular}{c} **[PERSON]** \\ **2017 [73]** \\ \end{tabular} \\ \hline \multirow{2}{*}{Salient360\({}^{\text{(2017)}}\) [(2018)]} & & & & & & & & & & \\ SVR [(2018)] & ✓ & & & ✓ & ✓ & ✓ & & & & & \\ SVR [(33)] & & ✓ & & & & & & & & & \\ Wild-360 [36] & & & & & & & & & & & & \\ LAF [(34)] & & & & & & & & & & & & \\ SVR [(77)] & & & & & & & & & & & & ✓ & \\ MIT [(78)] & & & & & & & & & & & ✓ & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dataset/method matrix for visual saliency estimation in omnidirectional images. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**Stimuli**} & \multicolumn{4}{c}{**Devices**} \\ \hline
**Dataset** & **Video/Image** & **CCI/Real** & **Resolution (Pixels)** & **Duration (Seconds)** & **Conditions** & **Display** & **Eye Tracker** \\ \hline Salient360t [41] & Mixed & Mixed & \(3000\times 1500\) + \(183,32\times 9166\) & 25 & Scated & Oculus DKE & SMI \\ SVR [(33)] & Image & CGI & \(8192\times 4096\) & 30 & Scated, standing & Oculus DKE & Puguli-labs, \\ Wild-360 [36] & Video & Real & \(1920\times 960\) & \(\sim\)20 (length) & N/A & PC screen & Tobii EyeX \\ LAF [(34)] & Image & Real & \(4096\times 2048\) & \(10,20\) & N/A & Oculus DKE & (none) \\ \hline \hline \multicolumn{8}{c}{**Cardinalities**} & \multicolumn{4}{c}{**Responses**} \\ \hline
**Dataset** & **Input Data** & **Users** & **Acquisitions** & **FM/SP** & **Head/Eyes** & **Project Page** \\ \hline Salient360t [41] & 85 images/19 videos & 63 & \(<\)32 maps/paths per stimulus & FM, SP & Head, eyes & [(79)] \\ SVR [(33)] & 22 images & 169 & 1980 maps/paths & FM, SP & Head, eyes & [(80)] \\ Wild-360 [(36)] & 85 videos (29 amm.) & 30 & 12.926 maps & FM & (manual) & [(81)] \\ LAF [(34)] & 21 images & 32 & 704 paths & SP & Head & [(82)] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Datasets for visual saliency in omnidirectional images and related characteristics (FM = Fixation maps, SP = Scapapaths).