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.

翻译：三维(3D)天体识别对于自主飞行器和机器人等智能自主物剂在无结构环境中有效运行至关重要,大多数最先进的方法都依赖相对密度的点云,对稀有点云而言,性能明显下降。无监督的域适应可以最大限度地缩小密度和稀有点云之间的差异,最小的未贴标签的点云,从而节省更多的稀少数据收集、批注和再培训费用。在这项工作中,我们提出了一个基于点云的天体识别新颖方法,在密集和稀有点云上采用最先进的方法进行竞争,同时只用密度点云进行训练。