A main challenge for tasks on panorama lies in the distortion of objects
among images. In this work, we propose a Distortion-Aware Monocular
Omnidirectional (DAMO) dense depth estimation network to address this challenge
on indoor panoramas with two steps. First, we introduce a distortion-aware
module to extract calibrated semantic features from omnidirectional images.
Specifically, we exploit deformable convolution to adjust its sampling grids to
geometric variations of distorted objects on panoramas and then utilize a strip
pooling module to sample against horizontal distortion introduced by inverse
gnomonic projection. Second, we further introduce a plug-and-play
spherical-aware weight matrix for our objective function to handle the uneven
distribution of areas projected from a sphere. Experiments on the 360D dataset
show that the proposed method can effectively extract semantic features from
distorted panoramas and alleviate the supervision bias caused by distortion. It
achieves state-of-the-art performance on the 360D dataset with high efficiency.

这篇论文提出了一种新的网络模型 (DAMO) 来解决室内全景图像中对象失真的问题，该模型采用扭曲卷积来处理几何失真，进一步引入了基于球形感知的权重矩阵来处理由球体投影引起的不均匀分布，在 360D 数据集上实现了效果最佳的表现。