  Depth estimation is a crucial step for 3D reconstruction with panorama images
in recent years. Panorama images maintain the complete spatial information but
introduce distortion with equirectangular projection. In this paper, we propose
an ACDNet based on the adaptively combined dilated convolution to predict the
dense depth map for a monocular panoramic image. Specifically, we combine the
convolution kernels with different dilations to extend the receptive field in
the equirectangular projection. Meanwhile, we introduce an adaptive
channel-wise fusion module to summarize the feature maps and get diverse
attention areas in the receptive field along the channels. Due to the
utilization of channel-wise attention in constructing the adaptive channel-wise
fusion module, the network can capture and leverage the cross-channel
contextual information efficiently. Finally, we conduct depth estimation
experiments on three datasets (both virtual and real-world) and the
experimental results demonstrate that our proposed ACDNet substantially
outperforms the current state-of-the-art (SOTA) methods. Our codes and model
parameters are accessed in https://github.com/zcq15/ACDNet.
