High-quality 3D object recognition is an important component of many vision
and robotics systems. We tackle the object recognition problem using two data
representations, to achieve leading results on the Princeton ModelNet
challenge. The two representations: 1. Volumetric representation: the 3D object
is discretized spatially as binary voxels - $1$ if the voxel is occupied and
$0$ otherwise. 2. Pixel representation: the 3D object is represented as a set
of projected 2D pixel images. Current leading submissions to the ModelNet
Challenge use Convolutional Neural Networks (CNNs) on pixel representations.
However, we diverge from this trend and additionally, use Volumetric CNNs to
bridge the gap between the efficiency of the above two representations. We
combine both representations and exploit them to learn new features, which
yield a significantly better classifier than using either of the
representations in isolation. To do this, we introduce new Volumetric CNN
(V-CNN) architectures.

利用体素表示法和像素表示法，使用卷积神经网络进行 3D 目标识别。使用新的 V-CNN 架构将两种表示法相结合，获得显著更好的分类器。