Local-to-Global Signature Descriptor for 3D Object Recognition

In this paper, we present a novel 3D descriptor that bridges the gap between global and local approaches. While local descriptors proved to be a more attractive choice for object recognition within cluttered scenes, they remain less discriminating exactly due to the limited scope of the local neighborhood. On the other hand, global descriptors can better capture relationships between distant points, but are generally affected by occlusions and clutter. So, we propose the Local-to-Global Signature (LGS) descriptor, which relies on surface point classification together with signature-based features to overcome the drawbacks of both local and global approaches. As our tests demonstrate, the proposed LGS can capture more robustly the exact structure of the objects while remaining robust to clutter and occlusion and avoiding sensitive, low-level features, such as point normals. The tests performed on four different datasets demonstrate the robustness of the proposed LGS descriptor when compared to three of the SOTA descriptors today: SHOT, Spin Images and FPFH. In general, LGS outperformed all three descriptors and for some datasets with a 50-70% increase in Recall.