  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.
