Abstract
Recent advances in deep convolutional neural networks
(CNNs) have motivated researchers to adapt CNNs to directly model points in 3D point clouds. Modeling local
structure has been proven to be important for the success of
convolutional architectures, and researchers exploited the
modeling of local point sets in the feature extraction hierarchy. However, limited attention has been paid to explicitly model the geometric structure amongst points in a
local region. To address this problem, we propose GeoCNN, which applies a generic convolution-like operation
dubbed as GeoConv to each point and its local neighborhood. Local geometric relationships among points are captured when extracting edge features between the center and
its neighboring points. We first decompose the edge feature extraction process onto three orthogonal bases, and
then aggregate the extracted features based on the angles
between the edge vector and the bases. This encourages the
network to preserve the geometric structure in Euclidean
space throughout the feature extraction hierarchy. GeoConv
is a generic and efficient operation that can be easily integrated into 3D point cloud analysis pipelines for multiple
applications. We evaluate Geo-CNN on ModelNet40 and
KITTI and achieve state-of-the-art performance.