To reconstruct an object surface from a set of surface points, a fast, practical, and efficient priority driven algorithm is presented. The key idea of the method is to consider the shape changes of an object at the boundary of the mesh growing area and to create a priority queue to the advancing front of the mesh area according to the changes. The mesh growing process is then driven by the priority queue for efficient surface reconstruction. New and practical triangulation criteria are also developed to support the priority driven strategy and to construct a new triangle at each step of mesh growing in real time. The quality and correctness of the created triangles will be guaranteed by the triangulation criteria and topological operations. The algorithm can reconstruct an object surface from unorganized surface points in a fast and reliable manner. Moreover, it can successfully construct the surface of the objects with complex geometry or topology. The efficiency and robustness of the proposed algorithm is validated by extensive experiments.

On surface reconstruction: A priority driven approach

In this paper, a new automated approach for the extraction of aboveground circular storage structures from near-nadir very high resolution satellite imagery is proposed. The approach focuses on the cast shadows of the circular structures and splits the boundaries of the shadow regions into curved segments using the chord-to-point distance accumulation technique. Thereafter, the curved segments are tested with newly developed constraints for being a part of a circular structure, and the ones that pass all of the constraints are considered as candidates. The reciprocal relations between the candidate segments are assessed by a developed mutual evidence test, and for the candidates that expose a relation, a robust circle fitting is applied. For the candidates having no such relations, an approach that further validates the circle evidence is developed. The approach consists in introducing regions-of-interest (ROIs) for each candidate segment and applying a circular Hough transform in each ROI, where the parameters of the transform are self-controlled. Experiments performed on 12 challenging Geoeye-1 test images selected from industrial areas reveal that the proposed approach accurately detects aboveground circular structures in complex industrial environments. Besides, the comparison of the results of the proposed approach with the results of two different circle detection approaches verifies the success and the robustness of the approach developed.

A New Approach for the Extraction of Aboveground Circular Structures From Near-Nadir VHR Satellite Imagery

Presently, 174 impact craters are proven on Earth, and of these 10 are located in Finland, 6 in Sweden and only 2 in Norway (Gardnos and Mjolnir). A pattern matching algorithm (correlation) based on 100 m digital elevation data was used in a regional study to discover circular depressions in the search for possible new Norwegian impact structures. By applying this technique to detect depressions of 5 – 10 km diameter in Finnmark, northern Norway, about 23 large circular structures were found in a 14,000 km area of Precambrian rocks. Circular features are clearly displayed in the detected structures. The large number of candidates in this area, however, makes field inspection inconvenient and time consuming, and supplementary screening methods should be considered to help reduce the number.

Automatic detection of circular depressions in digital elevation data in the search for potential Norwegian impact structures

This work aims to enhance a classic method for rigid registration, the iterative closest point (ICP), modifying the closest point search in order to consider approximated information of local geometry combined to the Euclidean distance, originally used. For this, a preprocessing stage is applied, in which the local geometry is encoded in second-order orientation tensors. We define the CTSF, a similarity factor between tensors. Our method uses a strategy of weight variation between the CTSF and the Euclidean distance, in order to establish correspondences. Quantitative tests were made in point clouds with different geometric features, with variable levels of additive noise and outliers and in partial overlapping situations. Results show that the proposed modification increases the convergence probability of the method for higher angles, making the method comparable to state-of-art techniques.

Wide Angle Rigid Registration Using a Comparative Tensor Shape Factor

Pairwise rigid registration aims to find the rigid transformation that best registers two surfaces represented by point clouds. This work presents a comparison between seven algorithms, with different strategies to tackle rigid registration tasks. We focus on the frame-to-frame problem, in which the point clouds are extracted from a video sequence with depth information generating partial overlapping 3D data. We use both point clouds and RGB-D video streams in the experimental results. The former is considered under different viewpoints with the addition of a case-study simulating missing data. Since the ground truth rotation is provided, we discuss four different metrics to measure the rotation error in this case. Among the seven considered techniques, the Sparse ICP and Sparse ICP-CTSF outperform the other five ones in the point cloud registration experiments without considering incomplete data. However, the evaluation facing missing data indicates sensitivity for these methods against this problem and favors ICP-CTSF in such situations. In the tests with video sequences, the depth information is segmented in the first step, to get the target region. Next, the registration algorithms are applied and the average root mean squared error, rotation and translation errors are computed. Besides, we analyze the robustness of the algorithms against spatial and temporal sampling rates. We conclude from the experiments using a depth video sequences that ICP-CTSF is the best technique for frame-to-frame registration.

/pdf/comparing-seven-methodogies-for-rigid-alignment-of-point-2lp4zowrug.pdf

Comparing Seven Methodogies for Rigid Alignment of Point Clouds with Focus on Frame-to-Frame Registration in Depth Sequences

We propose a new strategy to estimate surface normal information from highly noisy sparse data. Our approach is based on a tensor field morphologically adapted to infer normals. It acts as a three-dimensional structuring element of smooth surfaces. Robust orientation inference for all input elements is performed by morphological operations using the tensor field. A general normal estimator is defined by combining the inferred normals, their confidences and the tensor field. This estimator can be used to directly reconstruct the surface or give input normals to other reconstruction methods. We present qualitative and quantitative results to show the behavior of the original methods and ours. A comparative discussion of these results shows the efficiency of our propositions.

Smooth Surface Reconstruction Using Tensor Fields as Structuring Elements

The objective of this work is to identify geological circular forms, impact and volcano craters, using satellite images. The recognition of objects (circular forms) in the scene is the last step in a processing chain, which can be described in four phases: image preprocessing, pattern detection, pattern recognition, and identification of the targets (models). The paper presents the detection of circular forms on images including the south region of the Minas Gerais State in Brazil.

Paulo Pereira Martins

Papers

Smooth Surface Reconstruction Using Tensor Fields as Structuring Elements

Identification of patterns in satellite imagery: circular forms