A DASKL Descriptor via Encoding the Information of Keypoints and a 3D Local Surface for 3D Matching

Abstract

Three-dimensional matching is widely used in 3D vision tasks, such as 3D reconstruction, target recognition, and 3D model retrieval. The description of local features is the fundamental task of 3D matching; however, the descriptors only encode the surrounding surfaces of keypoints, and thus they cannot distinguish between similar local surfaces of objects. Therefore, we propose a novel local feature descriptor called deviation angle statistics of keypoints from local points and adjacent keypoints (DASKL). To encode a local surface fully, we first calculate a multiscale local reference axis (LRA); second, a local consistent strategy is used to redirect the normal direction, and the Poisson-disk sampling strategy is used to eliminate the redundancy in the data. Finally, the local surface is subdivided by two kinds of spatial features, and the histogram of the deviation angle between the LRA and the normal point in each subdivision space is generated. For the coding between keypoints, we calculate the LRA deviation angle between the nearest three keypoints and the adjacent keypoint. The performance of our DASKL descriptor is evaluated on several datasets (i.e., B3R, UWAOR, and LIDAR) with respect to Gaussian noise, varying mesh resolutions, clutter, and occlusion. The results show that our DASKL descriptor has achieved excellent performance in terms of description, robustness, and efficiency. Moreover, we further evaluate the generalization ability of the DASKL descriptor in a LIDAR real-scene dataset.