Building discriminative representations for 3D data has been an important
task in computer graphics and computer vision research. Convolutional Neural
Networks (CNNs) have shown to operate on 2D images with great success for a
variety of tasks. Lifting convolution operators to 3D (3DCNNs) seems like a
plausible and promising next step. Unfortunately, the computational complexity
of 3D CNNs grows cubically with respect to voxel resolution. Moreover, since
most 3D geometry representations are boundary based, occupied regions do not
increase proportionately with the size of the discretization, resulting in
wasted computation. In this work, we represent 3D spaces as volumetric fields,
and propose a novel design that employs field probing filters to efficiently
extract features from them. Each field probing filter is a set of probing
points --- sensors that perceive the space. Our learning algorithm optimizes
not only the weights associated with the probing points, but also their
locations, which deforms the shape of the probing filters and adaptively
distributes them in 3D space. The optimized probing points sense the 3D space
"intelligently", rather than operating blindly over the entire domain. We show
that field probing is significantly more efficient than 3DCNNs, while providing
state-of-the-art performance, on classification tasks for 3D object recognition
benchmark datasets.

通过使用体积场表示三维空间，使用场探测滤波器来高效地提取特征更为有效，与 3D CNNs 相比，在三维物体识别基准数据集的分类任务上展现出一流的性能。