Automatic Range Image Registration Research Articles

Multi-attribute statistics histograms for accurate and robust pairwise registration of range images

Registration of range images based on local shape features is widely adopted due to its validated effectiveness and robustness, while most existing local shape descriptors struggle to simultaneously achieve a pleasurable and balanced performance in terms of distinctiveness, robustness and time efficiency. This paper proposes a novel representation of 3D local surfaces, called multi-attribute statistics histograms (MaSH), for automatic registration of range images. MaSH comprises both spatial and geometric information characterizations. The characterization of spatial information is achieved via radial partitions in the 3D local support volume around the keypoint based on a local reference axis (LRA), creating a set of subspaces. While the encoding the shape geometry is performed by calculating statistical histograms of multiple faint correlated geometric attributes (i.e., local depth, normal deviation, and surface variation angle) for each subspace, so as to achieve information complementarity. Then, a robust rigid transformation estimation algorithm named 2-point based sample consensus with global constrain (2SAC-GC) is presented to tackle the problem of calculating an optimal transformation from the correspondence set with outliers. Finally, a coarse-to-fine registration method based on MaSH and 2SAC-GC is proposed for aligning range images. Experiments on both high-resolution (Laser Scanner) and low-resolution (Kinect) datasets report that, our method achieves a registration accuracy of 90.36% and 80.39% on the two datasets, respectively. It also exhibits strong robustness against noise and varying mesh resolutions. Furthermore, feature matching experiments show the over-all superiority of the proposed MaSH descriptor against the state-of-the-arts including the spin image, snapshots, THRIFT, FPFH, RoPS, LFSH and RCS descriptors.

A novel 3D vorticity based approach for automatic registration of low resolution range images

This paper tackles the problem of feature matching and range image registration. Our approach is based on a novel set of discriminating three-dimensional (3D) local features, named 3D-Vor (Vorticity). In contrast to conventional local feature representation techniques, which use the vector field (i.e. surface normals) to just construct their local reference frames, the proposed feature representation exploits the vorticity of the vector field computed at each point of the local surface to capture the distinctive characteristics at each point of the underlying 3D surface. The 3D-Vor descriptors of two range images are then matched using a fully automatic feature matching algorithm which identifies correspondences between the two range images. Correspondences are verified in a local validation step of the proposed algorithm and used for the pairwise registration of the range images. Quantitative results on low resolution Kinect 3D data (Washington RGB-D dataset) show that our proposed automatic registration algorithm is accurate and computationally efficient. The performance evaluation of the proposed descriptor was also carried out on the challenging low resolution Washington RGB-D (Kinect) object dataset, for the tasks of automatic range image registration. Reported experimental results show that the proposed local surface descriptor is robust to resolution, noise and more accurate than state-of-the-art techniques. It achieves 90% registration accuracy compared to 50%, 69.2% and 52% for spin image, 3D SURF and SISI/LD-SIFT descriptors, respectively.

Automatic Registration of Range Images by Using Shape-Index-Based Artificial Images

Automatic Registration of Range Images by Using Shape-Index-Based Artificial Images

Automatic Range Image Registration in the Markov Chain

In this paper, a novel entropy that can describe both long and short-tailed probability distributions of constituents of a thermodynamic system out of its thermodynamic limit is first derived from the Lyapunov function for a Markov chain. We then maximize this entropy for the estimation of the probabilities of possible correspondences established using the traditional closest point criterion between two overlapping range images. When we change our viewpoint to look carefully at the minimum solution to the probability estimate of the correspondences, the iterative range image registration process can also be modeled as a Markov chain in which lessons from past experience in estimating those probabilities are learned. To impose the two-way constraint, outliers are explicitly modeled due to the almost ubiquitous occurrence of occlusion, appearance, and disappearance of points in either image. The estimated probabilities of the correspondences are finally embedded into the powerful mean field annealing scheme for global optimization, leading the camera motion parameters to be estimated in the weighted least-squares sense. A comparative study using real images shows that the proposed algorithm usually outperforms the state-of-the-art ICP variants and the latest genetic algorithm for automatic overlapping range image registration.

Fast automatic registration of range images from 3D imaging systems using sphere targets

The use of 3D imaging systems (e.g., laser scanners) in construction has grown significantly in the past decade. Range images acquired with such systems often require registration. This paper describes an automatic method to rapidly locate spheres and perform a registration based on three pairs of matching points (centers of fitted spheres) in two range images. The proposed method is directly applicable for regularly gridded datasets obtained with instruments that are typically used for construction applications and whose maximum ranges are greater than 50 m. A lab was scanned from two locations at three different scan densities. Four spheres were located in the lab, and the total number of points hitting the four spheres was a small fraction (< 0.01%) of all the points in the dataset. At the highest scan density, the registration of two datasets with 6.4 × 10 6 and 3.4 × 10 6 points is obtained in less than 30 s. At the medium scan density, two range images with 1.6 × 10 6 and 0.8 × 10 6 points can be registered in less than 2 s.

A Novel Representation and Feature Matching Algorithm for Automatic Pairwise Registration of Range Images

Automatic registration of range images is a fundamental problem in 3D modeling of free-from objects. Various feature matching algorithms have been proposed for this purpose. However, these algorithms suffer from various limitations mainly related to their applicability, efficiency, robustness to resolution, and the discriminating capability of the used feature representation. We present a novel feature matching algorithm for automatic pairwise registration of range images which overcomes these limitations. Our algorithm uses a novel tensor representation which represents semi-local 3D surface patches of a range image by third order tensors. Multiple tensors are used to represent each range image. Tensors of two range images are matched to identify correspondences between them. Correspondences are verified and then used for pairwise registration of the range images. Experimental results show that our algorithm is accurate and efficient. Moreover, it is robust to the resolution of the range images, the number of tensors per view, the required amount of overlap, and noise. Comparisons with the spin image representation revealed that our representation has more discriminating capabilities and performs better at a low resolution of the range images.

Improving ICP with easy implementation for free-form surface matching

Automatic range image registration and matching is an attractive but unresolved problem in both the machine vision and pattern recognition literature. Since automatic range image registration and matching is inherently a very difficult problem, the algorithms developed are becoming more and more complicated. In this paper, we propose a novel practical algorithm for automatic free-form surface matching. This method directly manipulates the possible point matches established by the traditional ICP criterion based on both the collinearity and closeness constraints without any feature extraction, image pre-processing, or motion estimation from outliers corrupted data. A comparative study based on a large number of real range images has shown the accuracy and robustness of the novel algorithm.