Abstract
Unlike on images, semantic learning on 3D point clouds
using a deep network is challenging due to the naturally
unordered data structure. Among existing works, PointNet has achieved promising results by directly learning on
point sets. However, it does not take full advantage of a
point’s local neighborhood that contains fine-grained structural information which turns out to be helpful towards
better semantic learning. In this regard, we present two
new operations to improve PointNet with a more efficient
exploitation of local structures. The first one focuses on
local 3D geometric structures. In analogy to a convolution kernel for images, we define a point-set kernel as a
set of learnable 3D points that jointly respond to a set of
neighboring data points according to their geometric affi-
nities measured by kernel correlation, adapted from a similar technique for point cloud registration. The second one
exploits local high-dimensional feature structures by recursive feature aggregation on a nearest-neighbor-graph computed from 3D positions. Experiments show that our network can efficiently capture local information and robustly
achieve better performances on major datasets. Our code
is available at http://www.merl.com/research/
license#KCNet