Computer Science > Computer Vision and Pattern Recognition
[Submitted on 23 Sep 2020 (v1), last revised 27 May 2021 (this version, v3)]
Title:2D-3D Geometric Fusion Network using Multi-Neighbourhood Graph Convolution for RGB-D Indoor Scene Classification
View PDFAbstract: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.
Submission history
From: Albert Mosella-Montoro [view email][v1] Wed, 23 Sep 2020 13:58:12 UTC (682 KB)
[v2] Sun, 14 Feb 2021 16:27:55 UTC (708 KB)
[v3] Thu, 27 May 2021 10:06:33 UTC (708 KB)
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