A Deep Learning Method for 3D Object Classification Using the Wave Kernel Signature and A Center Point of the 3D-Triangle Mesh

• Main Authors: Long Hoang, Suk-Hwan Lee, Oh-Heum Kwon, Ki-Ryong Kwon
• Format: Article
• Language: English
• Published: MDPI AG 2019-10-01
• Series: Electronics
• Subjects: deep learning applications; convolutional neural networks; 3d object classification; 3d triangle mesh; center point; wave kernel signature
• Online Access: https://www.mdpi.com/2079-9292/8/10/1196

Computer vision recently has many applications such as smart cars, robot navigation, and computer-aided manufacturing. Object classification, in particular 3D classification, is a major part of computer vision. In this paper, we propose a novel method, wave kernel signature (WKS) and a center point (CP) method, which extracts color and distance features from a 3D model to tackle 3D object classification. The motivation of this idea is from the nature of human vision, which we tend to classify an object based on its color and size. Firstly, we find a center point of the mesh to define distance feature. Secondly, we calculate eigenvalues from the 3D mesh, and WKS values, respectively, to capture color feature. These features will be an input of a 2D convolution neural network (CNN) architecture. We use two large-scale 3D model datasets: ModelNet10 and ModelNet40 to evaluate the proposed method. Our experimental results show more accuracy and efficiency than other methods. The proposed method could apply for actual-world problems like autonomous driving and augmented/virtual reality.