用球面采样让2D预训练模型直接处理全景图分割，效果更好。
360-Degree Full-view Image Segmentation by Spherical Convolution compatible with Large-scale Planar Pre-trained Models
- 提出球面离散采样方法，适配现有2D预训练模型
- 在Stanford2D3D数据集上实现良好分割性能
- 适合想复用2D模型做全景图像任务的研究者
由于缺乏百万级大规模全景图像数据集，当前全景图像任务多依赖现有的二维预训练图像基准模型作为主干网络。然而，这些模型无法识别全景图像固有的畸变和不连续性，导致性能下降。本文提出一种新型球面采样方法，使现有为二维图像设计的预训练模型可直接用于全景图像。该方法基于预训练模型权重进行球面离散采样，有效缓解畸变并获得良好的初始训练值。此外，将该采样方法应用于全景图像分割，利用球面模型提取的特征作为特定通道注意力的掩码，在常用室内数据集Stanford2D3D上取得优异结果。
原文摘要 · Abstract (English)
Due to the current lack of large-scale datasets at the million-scale level, tasks involving panoramic images predominantly rely on existing two-dimensional pre-trained image benchmark models as backbone networks. However, these networks are not equipped to recognize the distortions and discontinuities inherent in panoramic images, which adversely affects their performance in such tasks. In this paper, we introduce a novel spherical sampling method for panoramic images that enables the direct utilization of existing pre-trained models developed for two-dimensional images. Our method employs spherical discrete sampling based on the weights of the pre-trained models, effectively mitigating distortions while achieving favorable initial training values. Additionally, we apply the proposed sampling method to panoramic image segmentation, utilizing features obtained from the spherical model as masks for specific channel attentions, which yields commendable results on commonly used indoor datasets, Stanford2D3D.
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