SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion
  for 3D Scene Graph Alignment and Its Downstream Tasks
Yaxu Xie Alain Pagani Didier Stricker
Scene graphs have been recently introduced into 3D spatial understanding as a
comprehensive representation of the scene. The alignment between 3D scene
graphs is the first step of many downstream tasks such as scene graph aided
point cloud registration, mosaicking, overlap checking, and robot navigation.
In this work, we treat 3D scene graph alignment as a partial graph-matching
problem and propose to solve it with a graph neural network. We reuse the
geometric features learned by a point cloud registration method and associate
the clustered point-level geometric features with the node-level semantic
feature via our designed feature fusion module. Partial matching is enabled by
using a learnable method to select the top-k similar node pairs. Subsequent
downstream tasks such as point cloud registration are achieved by running a
pre-trained registration network within the matched regions. We further propose
a point-matching rescoring method, that uses the node-wise alignment of the 3D
scene graph to reweight the matching candidates from a pre-trained point cloud
registration method. It reduces the false point correspondences estimated
especially in low-overlapping cases. Experiments show that our method improves
the alignment accuracy by 10~20% in low-overlap and random transformation
scenarios and outperforms the existing work in multiple downstream tasks.