Efficient Object Pose Estimation in 3D Point Clouds using Sparse Hash-Maps and Point-Pair Features

This paper presents an image processing pipeline for object pose estimation (3D translation and rotation) in 3D point clouds. In comparison to the state of the art algorithms, the presented approach uses sparse hash-maps in order to reduce the number of hypotheses and the computational costs as early in the process as possible. Firstly, the image processing pipeline starts with spectral clustering to estimate object clusters. Then, the sparse hash-maps from point-pair features are used to generate hypotheses for each object. After that, each hypothesis is evaluated by considering the visual appearance (shape and colour) with a quality function which returns a comparable confidence value for every hypotheses. The pipeline is able to detect partially occluded and fully visible objects. The proposed approach is evaluated with online available 3D datasets.