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.

提出了一种 2D-3D 融合策略，将 3D 几何特征和 2D 纹理特征相结合，实现场景分类任务中的多模态融合，并通过多领域图卷积和最近体素池化等策略提高性能。实验结果表明，该方法在 RGB-D 室内场景分类任务中优于当前的技术水平。