Multi-modal fusion has been proved to help enhance the performance of scene classification tasks. This paper presents a 2D–3D Fusion stage that combines 3D Geometric Features with 2D Texture Features obtained by 2D Convolutional Neural Networks. To get a robust 3D Geometric embedding, a network that uses two novel layers is proposed. The first layer, Multi-Neighbourhood Graph Convolution, aims to learn a more robust geometric descriptor of the scene combining two different neighbourhoods: one in the Euclidean space and the other in the Feature space. The second proposed layer, Nearest Voxel Pooling, improves the performance of the well-known Voxel Pooling. Experimental results, using NYU-Depth-V2 and SUN RGB-D datasets, show that the proposed method outperforms the current state-of-the-art in RGB-D indoor scene classification task.

事实证明，多模式融合有助于增强场景分类任务的性能。本文介绍了将3D几何特征与2D卷积神经网络获得的2D纹理特征相结合的2D–3D融合阶段。为了获得鲁棒的3D几何嵌入，提出了使用两个新颖层的网络。第一层是“多邻域图卷积”，旨在学习结合两个不同邻域的场景的更健壮的几何描述符：一个在欧几里得空间中，另一个在特征空间中。提议的第二层，最近的体素池化，提高了众所周知的体素池化的性能。使用NYU-Depth-V2和SUN RGB-D数据集进行的实验结果表明，所提出的方法在RGB-D室内场景分类任务中优于当前的最新技术。