Mini-PointNetPlus: a local feature descriptor in deep learning model for
  3d environment perception
Chuanyu Luo Nuo Cheng Sikun Ma Jun Xiang Xiaohan Li Shengguang Lei Pu Li
Common deep learning models for 3D environment perception often use
pillarization/voxelization methods to convert point cloud data into
pillars/voxels and then process it with a 2D/3D convolutional neural network
(CNN). The pioneer work PointNet has been widely applied as a local feature
descriptor, a fundamental component in deep learning models for 3D perception,
to extract features of a point cloud. This is achieved by using a symmetric
max-pooling operator which provides unique pillar/voxel features. However, by
ignoring most of the points, the max-pooling operator causes an information
loss, which reduces the model performance. To address this issue, we propose a
novel local feature descriptor, mini-PointNetPlus, as an alternative for
plug-and-play to PointNet. Our basic idea is to separately project the data
points to the individual features considered, each leading to a permutation
invariant. Thus, the proposed descriptor transforms an unordered point cloud to
a stable order. The vanilla PointNet is proved to be a special case of our
mini-PointNetPlus. Due to fully utilizing the features by the proposed
descriptor, we demonstrate in experiment a considerable performance improvement
for 3D perception.