  Three-dimensional (3D) object recognition is crucial for intelligent
autonomous agents such as autonomous vehicles and robots alike to operate
effectively in unstructured environments. Most state-of-art approaches rely on
relatively dense point clouds and performance drops significantly for sparse
point clouds. Unsupervised domain adaption allows to minimise the discrepancy
between dense and sparse point clouds with minimal unlabelled sparse point
clouds, thereby saving additional sparse data collection, annotation and
retraining costs. In this work, we propose a novel method for point cloud based
object recognition with competitive performance with state-of-art methods on
dense and sparse point clouds while being trained only with dense point clouds.
