  Transformation Synchronization is the problem of recovering absolute
transformations from a given set of pairwise relative motions. Despite its
usefulness, the problem remains challenging due to the influences from noisy
and outlier relative motions, and the difficulty to model analytically and
suppress them with high fidelity. In this work, we avoid handcrafting robust
loss functions, and propose to use graph neural networks (GNNs) to learn
transformation synchronization. Unlike previous works which use complicated
multi-stage pipelines, we use an iterative approach where each step consists of
a single weight-shared message passing layer that refines the absolute poses
from the previous iteration by predicting an incremental update in the tangent
space. To reduce the influence of outliers, the messages are weighted before
aggregation. Our iterative approach alleviates the need for an explicit
initialization step and performs well with identity initial poses. Although our
approach is simple, we show that it performs favorably against existing
handcrafted and learned synchronization methods through experiments on both
SO(3) and SE(3) synchronization.
