Local-to global point cloud registration is a challenging task due to the substantial differences between these two types of data, and the different techniques used to acquire them. Global clouds cover large-scale environments and are usually acquired aerially, e.g., 3D modeling of a city using Airborne Laser Scanning (ALS). In contrast, local clouds are often acquired from ground level and at a much smaller range, for example, using Terrestrial Laser Scanning (TLS). The differences are often manifested in point density distribution, occlusions nature, and measurement noise. As a result of these differences, existing point cloud registration approaches, such as keypoint-based registration, tend to fail. We improve upon a different approach, recently proposed, based on converting the global cloud into a viewpoint-based cloud dictionary. We propose a local-toglobal registration method where we replace the dictionary clouds with viewpoint descriptors, consisting of panoramic range-images. We then use an efficient dictionary search in the Discrete Fourier Transform (DFT) domain, using phase correlation, to rapidly find plausible transformations from the local to the global reference frame. We demonstrate our method's significant advantages over the previous cloud dictionary approach, in terms of computational efficiency and memory requirements. In addition, We show its superior registration performance in comparison to a state-ofthe- art, keypoint-based method (FPFH). For the evaluation, we use a challenging dataset of TLS local clouds and an ALS large-scale global cloud, in an urban environment.

Closed-form expressions for a new set of 3D rotation invariants that are linear, quadratic, and angular combinations of implicit polynomial (IP) coefficients are developed. Based on these invariants, we propose a 3D object recognition method that outperforms recognition based on IP fitting after pose estimation, and the MPEG-7 SSD technique.

This work deals with fitting 2D and 3D implicit polynomials (IPs) to 2D curves and 3D surfaces, respectively. The zero-set of the polynomial is determined by the IP coefficients and describes the data. The polynomial fitting algorithms proposed in this paper aim at reducing the sensitivity of the polynomial to coefficient errors. Errors in coefficient values may be the result of numerical calculations, when solving the fitting problem or due to coefficient quantization. It is demonstrated that the effect of reducing this sensitivity also improves the fitting tightness and stability of the proposed two algorithms in fitting noisy data, as compared to existing algorithms like the well-known 3L and gradient-one algorithms. The development of the proposed algorithms is based on an analysis of the sensitivity of the zero-set to small coefficient changes and on minimizing a bound on the maximal error for one algorithm and minimizing the error variance for the second. Simulation results show that the proposed algorithms provide a significant reduction in fitting errors, particularly when fitting noisy data of complex shapes with high order polynomials, as compared to the performance obtained by the abovementioned existing algorithms.

Presents an approach to contour representation and coding. It consists of an improved fitting of high-degree (4/sup th/ to 18/sup th/) implicit polynomials (IPs) to the contour which is robust to coefficient quantization. The proposed approach to solve the fitting problem is a modification of the 3L linear solution developed by Lei et al. (1997) and is more robust to noise and to coefficient quantization. We use an analytic approach to limit the maximal fitting error between each data point and the zero-set generated by the quantized polynomial coefficients. We than show that consideration of the quantization error (which led to a specific sensitivity criterion) also brought about a significant improvement in fitting IPs to noisy data, as compared to the 3L algorithm.

A heretofore unsolved challenge is the completely automatic and accurate estimation of road boundaries in aerial images when the roads may be partially or completely locally occluded and clutter may be prevalent. We introduce a roadfinder that is effective in meeting this challenge. The roadfinder begins with one or more seeds on each long road, and then accurately estimates the remaining boundaries, which can be found completely automatically by the algorithm described by Barzohar and Cooper (see IEEE PAMI, vol.18, no.7, p.407-21, 1996). The algorithm is robust to missing boundary edges on one side of the road and on both sides of the road simultaneously. (These arise from shadows and occlusion by trees, poles, small structures, etc.) It is also robust to clutter within the road caused by cars or trucks, and to clutter resulting from intersecting or close parallel roads. The algorithm is based on simple clutter and occlusion models and a combined multihypothesis generalized Kalman filter (MGKF).

This paper presents an automated approach to finding main roads in aerial images. The approach is to build geometric-probabilistic models for road image generation. We use Gibbs distributions. Then, given an image, roads are found by MAP (maximum a posteriori probability) estimation. The MAP estimation is handled by partitioning an image into windows, realizing the estimation in each window through the use of dynamic programming, and then, starting with the windows containing high confidence estimates, using dynamic programming again to obtain optimal global estimates of the roads present. The approach is model-based from the outset and is completely different than those appearing in the published literature. It produces two boundaries for each road, or four boundaries when a mid-road barrier is present.

This paper treats some of the problem in recognizing the geometry of objects composed of groups of curves that undergo arbitrary affine transformations, providing the objects can be invariantly segmented into groups of curves representable by third degree implicit polynomials. As an illustrative example, the authors treat the problem of representing and recognizing portions of roads in aerial images. The approach the authors develop is to represent each road segment in a disc by a third degree implicit-polynomial, and recognition of road geometry is then Bayesian recognition of a vector of mutual algebraic invariants for the polynomials within such a disc. The mutual affine invariants developed and used are for pairs of polynomials. This is a new powerful approach to dealing with the recognition of complex curves.

An automated approach to finding main roads in aerial images is presented. The approach is to build geometric-probabilistic models for road image generation. Gibbs distributions are used. Then, given an image, roads are found by MAP (maximum aposteriori probability) estimation. The MAP estimation is handled by partitioning an image into windows, realizing the estimation in each window through the use of dynamic programming, and then, starting with the windows containing high confidence estimates, using dynamic programming again to obtain optimal global estimates of the roads present. The approach is model-based from the outset. It produces two boundaries for each road, or four boundaries when a midroad barrier is present. >