## 2 Related Studies  
Many state-of-the-art models have been proposed for the use of 360\({}^{\circ}\) omnidirectional images in classification [28], object detection [29], and depth estimation tasks [30]. [PERSON] et al. [31] transformed EPR images into spherical images which were projected onto four sub-planes by perspective projection. Each plane image was then fed into YOLOV2 [32] for subsequent processing. Finally, the information learned by each network was aggregated. This multi-projection processing method reduced the influence of distortion, but resulted in greater computational overhead. [PERSON] et al. [33] and [PERSON] et al. [34] applied another method that could mitigate the effects of distortion, involving the projection of a 360\({}^{\circ}\) omnidirectional image onto the six sub-faces of a cube by perspective projection. Although the distortion was further reduced, the images were not continuous at the boundaries of each cube face and required subsequent processing. [PERSON] et al. [20] proposed SphereNet, which resampled ERP images (360\({}^{\circ}\) omnidirectional images after ERP-processing) to address distortion and left-right edge discontinuity. The method used converted the ERP image into a spherical image. The spherical image was projected onto the tangent plane via a gnomonic projection [35]. Then the convolution kernel sampling position was determined on the tangent plane. Finally, the location of the convolutional kernel sampling in the ERP image was obtained by back projection. [PERSON] et al. [36] pointed out that SphereNet introduced an interpolation assumption that could affect the performance of the network as it deepened. [PERSON] et al. [37] suggested that the distortion of ERP images was mainly related to the latitude of 360\({}^{\circ}\) omnidirectional images. These authors then trained a convolutional kernel for each row of images. This method involved two networks: the first network enabled the 360\({}^{\circ}\) omnidirectional image to be first projected onto multiple tangent planes. Then the trained source network Faster-RCNN [38] was used to extract features. The second network was used to directly extract features from ERP images. [PERSON] et al. used features extracted from each layer in the two networks to use the mean square error for back propagation to adjust the weights of the convolution kernels; the learned network SPHCONV [37] was able to handle distortion. Because the convolution kernel of each row in SPHCONV is not shared and needs to be learned, it generates a huge computational overhead. To solve this problem, [PERSON] et al. proposed a new network KTN [36], where several rows share a convolution kernel. Whether using SPHCONV or KTN, the source network needed to be used for training before it could be transferred to the target tasks, which is time-consuming. [PERSON] et al. [21] proposed a representation method based on an icosahedron in which a 360\({}^{\circ}\) omnidirectional image was projected onto the icosahedron. As the subdivision of the icosahedron became finer, the effect of distortion was smaller and smaller. However, the computational overhead increased as the degree of subdivision became finer and finer because the convolution operation was not suitable for images represented by an icosahedron. [PERSON] et al. proposed a new method that involved new convolution and pooling operations. Although this projection method reduced the effect of distortion, it entailed huge computational costs. [PERSON] et al. [24] performed depth estimation on 360\({}^{\circ}\) omnidirectional images by projecting the 360\({}^{\circ}\) omnidirectional images to multiple planes through gnomonic projection, using Transformer [39] to perform efficient feature extraction and, finally, completing the depth estimation task by fusing the output using OmniFusion [24]. Semantic segmentation has became an important component of perception in autonomous driving and 360\({}^{\circ}\) omnidirectional images can provide information on a broader field of view. However, [PERSON] et al.