To better address challenging issues of the irregularity and inhomogeneity
inherently present in 3D point clouds, researchers have been shifting their
focus from the design of hand-craft point feature towards the learning of 3D
point signatures using deep neural networks for 3D point cloud classification.
Recent proposed deep learning based point cloud classification methods either
apply 2D CNN on projected feature images or apply 1D convolutional layers
directly on raw point sets. These methods cannot adequately recognize
fine-grained local structures caused by the uneven density distribution of the
point cloud data. In this paper, to address this challenging issue, we
introduced a density-aware convolution module which uses the point-wise density
to re-weight the learnable weights of convolution kernels. The proposed
convolution module is able to fully approximate the 3D continuous convolution
on unevenly distributed 3D point sets. Based on this convolution module, we
further developed a multi-scale fully convolutional neural network with
downsampling and upsampling blocks to enable hierarchical point feature
learning. In addition, to regularize the global semantic context, we
implemented a context encoding module to predict a global context encoding and
formulated a context encoding regularizer to enforce the predicted context
encoding to be aligned with the ground truth one. The overall network can be
trained in an end-to-end fashion with the raw 3D coordinates as well as the
height above ground as inputs. Experiments on the International Society for
Photogrammetry and Remote Sensing (ISPRS) 3D labeling benchmark demonstrated
the superiority of the proposed method for point cloud classification. Our
model achieved a new state-of-the-art performance with an average F1 score of
71.2% and improved the performance by a large margin on several categories.

本文提出了一种基于密度感知卷积和上下文编码的多尺度全卷积神经网络方法，用于 3D 点云分类，同时在 ISPRS 3D 标注基准上实现了新的最优性能。