论文标题
在360°全向图像中进行显着对象检测的多投影融合和改进网络
Multi-Projection Fusion and Refinement Network for Salient Object Detection in 360° Omnidirectional Image
论文作者
论文摘要
显着对象检测(SOD)旨在确定图像中最有吸引力的对象。随着虚拟现实技术的发展,已广泛使用了360°全向图像,但是由于其严重的扭曲和复杂的场景,很少研究360°全向图像中的SOD任务。在本文中,我们提出了一个多投影融合和改进网络(MPFR-NET),以检测360°全向图像中的显着物体。与现有方法不同,等于等值的投影图像和四个相应的立方体不折叠图像同时嵌入网络中,作为输入,在这里,立方体无折叠的图像不仅为等效角投影图像提供补充信息,还可以确保Cube-Map-Map投射的对象完整性。为了充分利用这两种投影模式,动态加权融合(DWF)模块旨在从间和内部特征的角度以互补和动态的方式自适应地整合不同投影的特征。此外,为了充分探索编码器和解码器功能之间的相互作用方式,过滤和细化(FR)模块旨在抑制功能本身与功能之间的冗余信息。两个全向数据集的实验结果表明,所提出的方法在定性和定量上都优于最先进的方法。
Salient object detection (SOD) aims to determine the most visually attractive objects in an image. With the development of virtual reality technology, 360° omnidirectional image has been widely used, but the SOD task in 360° omnidirectional image is seldom studied due to its severe distortions and complex scenes. In this paper, we propose a Multi-Projection Fusion and Refinement Network (MPFR-Net) to detect the salient objects in 360° omnidirectional image. Different from the existing methods, the equirectangular projection image and four corresponding cube-unfolding images are embedded into the network simultaneously as inputs, where the cube-unfolding images not only provide supplementary information for equirectangular projection image, but also ensure the object integrity of the cube-map projection. In order to make full use of these two projection modes, a Dynamic Weighting Fusion (DWF) module is designed to adaptively integrate the features of different projections in a complementary and dynamic manner from the perspective of inter and intra features. Furthermore, in order to fully explore the way of interaction between encoder and decoder features, a Filtration and Refinement (FR) module is designed to suppress the redundant information between the feature itself and the feature. Experimental results on two omnidirectional datasets demonstrate that the proposed approach outperforms the state-of-the-art methods both qualitatively and quantitatively.