Single-Panorama Classification of 3D Objects Using Horizontally Stacked Dilated Convolutions

This paper presents a single-image approach for classifying 3D objects represented as meshes. Our method centers a virtual spherical camera at the object’s centroid and casts omnidirectional rays. Then, it computes local geometry information of each ray’s first and last intersection points, generating a single multi-channel equirectangular (ERP) image per object. We propose a convolutional block named Horizontally Stacked Dilated Convolution (HSDC) to handle ERP distortions and introduce a classifier built upon these blocks. Our experiments in popular datasets show that the results produced by our method are competitive or better than state-of-the-art voxel-and point-based methods, being the best among single-view approaches. Code is available at https://github.com/rmstringhini/HSDCNet.