通道与空间注意力结合的室内场景三维重建

3D Reconstruction of Indoor Scene Combining Channel and Spatial Attention

平面分割和参数估计是基于单幅室内场景图像分段平面三维重建的关键技术。目前基于卷积神经网络的方法难以获取全局上下文信息且未充分考虑特征通道间的关系,易造成小平面分割不准确及平面细节丢失,导致对应区域的参数估计出现较大误差。为此,提出了一种特征通道和空间注意力机制融合的卷积神经网络模型,该模型利用通道注意力对网络通道间特征响应进行标定,再结合空间注意力提取编码器中的空间语义信息,使网络也能聚焦于小平面和平面细节。实验表明,提出的方法能显著提高平面分割精度,且深度预测精度达到93.57%,有效提升了场景三维重建精度。

Segmentation and parameters estimation of planes are the key steps of 3D piecewise planar reconstruction from a single indoors image.Most existing works of CNN-based methods ingore the global context information and the relationship between feature channels,so it is easy to attain inaccurate segmentation of small planes and make plane details lost.resulting large errors of the parameter estimation of corresponding regions.In this paper,we propose a novel CNN model with fusion feature channel attention and spatial attention.In this model,the feature response between network channels is calibrated by using channel attention,and spatial semantic information is extracted in the encoder with combining spatial attention,which focus the network on the small planes and plane's detail.Experimental results show that the proposed method can significantly improve the accuracy of plane segmentation,and the accuracy of depth prediction reaches 93.57%,which effectively improves the performance of 3D scene reconstruction.