2 research outputs found
A Novel SDASS Descriptor for Fully Encoding the Information of 3D Local Surface
Local feature description is a fundamental yet challenging task in 3D
computer vision. This paper proposes a novel descriptor, named Statistic of
Deviation Angles on Subdivided Space (SDASS), of encoding geometrical and
spatial information of local surface on Local Reference Axis (LRA). In terms of
encoding geometrical information, considering that surface normals, which are
usually used for encoding geometrical information of local surface, are
vulnerable to various nuisances (e.g., noise, varying mesh resolutions etc.),
we propose a robust geometrical attribute, called Local Minimum Axis (LMA), to
replace the normals for generating the geometrical feature in our SDASS
descriptor. For encoding spatial information, we use two spatial features for
fully encoding the spatial information of a local surface based on LRA which
usually presents high overall repeatability than Local Reference Axis (LRF).
Besides, an improved LRA is proposed for increasing the robustness of our SDASS
to noise and varying mesh resolutions. The performance of the SDASS descriptor
is rigorously tested on four popular datasets. The results show that our
descriptor has a high descriptiveness and strong robustness, and its
performance outperform existing algorithms by a large margin. Finally, the
proposed descriptor is applied to 3D registration. The accurate result further
confirms the effectiveness of our SDASS method.Comment: 21 pages, 15 figure
A Comprehensive Performance Evaluation for 3D Transformation Estimation Techniques
3D local feature extraction and matching is the basis for solving many tasks
in the area of computer vision, such as 3D registration, modeling, recognition
and retrieval. However, this process commonly draws into false correspondences,
due to noise, limited features, occlusion, incomplete surface and etc. In order
to estimate accurate transformation based on these corrupted correspondences,
numerous transformation estimation techniques have been proposed. However, the
merits, demerits and appropriate application for these methods are unclear
owing to that no comprehensive evaluation for the performance of these methods
has been conducted. This paper evaluates eleven state-of-the-art transformation
estimation proposals on both descriptor based and synthetic correspondences. On
descriptor based correspondences, several evaluation items (including the
performance on different datasets, robustness to different overlap ratios and
the performance of these technique combined with Iterative Closest Point (ICP),
different local features and LRF/A techniques) of these methods are tested on
four popular datasets acquired with different devices. On synthetic
correspondences, the robustness of these methods to varying percentages of
correct correspondences (PCC) is evaluated. In addition, we also evaluate the
efficiencies of these methods. Finally, the merits, demerits and application
guidance of these tested transformation estimation methods are summarized