Curvature Augmented Deep Learning For 3D Object Recognition

Keywords

3D Object Recognition; Computational Geometry; Convolutional Neural Networks; Deep Learning

Abstract

This paper presents a new method to incorporate shape information into convolutional neural network (CNN)s for 3D object recognition. Voxel CNNs have been very successful with the task of 3D object recognition. However, continuous shape information that is useful for recognition is often lost in their conversion to a voxel representation. We propose a single dimensional feature that can be applied to voxel CNNs. This paper presents a novel rotation-invariant feature based on mean curvature that improves shape recognition for voxel CNNs. We augment the recent voxel CNN Octnet architecture with our feature and demonstrate a 1 % overall accuracy increase on the ModelNet 10 dataset.

Publication Date

8-29-2018

Publication Title

Proceedings - International Conference on Image Processing, ICIP

Number of Pages

3648-3652

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICIP.2018.8451487

Socpus ID

85062898142 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85062898142

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