3D object recognition accuracy can be improved by learning the multi-scale
spatial features from 3D spatial geometric representations of objects such as
point clouds, 3D models, surfaces, and RGB-D data. Current deep learning
approaches learn such features either using structured data representations
(voxel grids and octrees) or from unstructured representations (graphs and
point clouds). Learning features from such structured representations is
limited by the restriction on resolution and tree depth while unstructured
representations creates a challenge due to non-uniformity among data samples.
In this paper, we propose an end-to-end multi-level learning approach on a
multi-level voxel grid to overcome these drawbacks. To demonstrate the utility
of the proposed multi-level learning, we use a multi-level voxel representation
of 3D objects to perform object recognition. The multi-level voxel
representation consists of a coarse voxel grid that contains volumetric
information of the 3D object. In addition, each voxel in the coarse grid that
contains a portion of the object boundary is subdivided into multiple
fine-level voxel grids. The performance of our multi-level learning algorithm
for object recognition is comparable to dense voxel representations while using
significantly lower memory.

本文提出了一种基于多层体素网格的端到端多级学习方法来解决现有 3D 目标识别中结构化和非结构化数据表示的缺陷，结果表明该方法在节约内存的同时能够实现与密集体素表示相当的目标识别性能。