Learning continuous object representations from point cloud data

Continuous representations of objects have always been used in robotics in the form of geometric primitives and surface models. Recently, learning techniques have emerged which allow more complex continuous representations to be learned from data, but these learning techniques require training data in the form of watertight meshes which restricts their application as meshes of this form are difficult to obtain from real data. This paper proposes a modification to existing methods that allows real world point cloud data to be used for training these surface representations allowing the techniques to be used in broader applications. The modification is evaluated on ModelNet10 to quantify the difference between the existing and the proposed methods as well as on a novel precision agriculture dataset that has been released publicly to show the modification's applicability to new areas. The proposed method enables obtaining training data from real world sensors that produce point clouds rather than requiring an expensive meshing step which may not be possible for some applications. This opens the possibility of using techniques like this for complex shapes in areas like grasping and agricultural data collection.