UVS-CNNs: Constructing general convolutional neural networks on quasi-uniform spherical images

Omnidirectional images, also known as spherical images, offer a significant advantage for the environmental sensing of mobile robots due to their wide field of view. However, previous studies of constructing convolutional neural networks on spherical images have been limited by non-uniform pixel sampling, leading to suboptimal performance in semantic segmentation. To address this issue, a novel pixel segmentation approach is proposed to achieve near-uniform pixel distribution across the entire spherical surface. The corresponding convolution operation for the resulting image is designed as well, which extends the capabilities of spherical CNNs from semantic segmentation to more complex tasks such as instance segmentation. The method is evaluated on the Stanford 2D3DS dataset and shows superior performance compared to conventional spherical CNNs. Furthermore, the method also achieves impressive instance segmentation results on our experimental LiDAR data, demonstrating the general feasibility of our approach for common CNN tasks. The related code and dataset are released in the following link: https://github.com/YoungRainy/UVS-U-Net.