Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection

Shahbazi, Mozhdeh; Sohn, Gunho; Théau, Jérôme

doi:10.3390/a10030087

Cited by 10 publications

(3 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Initial image matching is performed using a modified SIFT algorithm. A robust method based on an evolutionary algorithm is used to estimate the two-view epipolar geometry of image pairs and identify the inlier corresponding points [3] . Then, a sequential approach is applied to convert two-view parameters of epipolar-geometry between image pairs first to relative orientation parameters (ROPs) and, then, to exterior orientation parameters.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Unmanned aerial image dataset: Ready for 3D reconstruction

et al. 2019

Self Cite

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) have become popular platforms for collecting various types of geospatial data for various mapping, monitoring and modelling applications. With the advancement of imaging and computing technologies, a vast variety of photogrammetric, computer-vision and, nowadays, end-to-end learning workflows are introduced to produce three-dimensional (3D) information in form of digital surface and terrain models, textured meshes, rectified mosaics, CAD models, etc. These 3D products might be used in applications where accuracy and precision play a vital role, e.g. structural health monitoring. Therefore, extensive tests against data with relevant characteristics and reliable ground-truth are required to assess and ensure the performance of 3D modelling workflows. This article describes the images collected by a customized unmanned aerial vehicle (UAV) system from an open-pit gravel mine accompanied with additional data that will allow implementing and evaluating any structure-from-motion or photogrammetric approach for sparse or dense 3D reconstruction. This dataset includes total of 158 high-quality images captured with more than 80% endlap and spatial resolution higher than 1.5 cm, the 3D coordinates of 109 ground control points and checkpoints, 2D coordinates of more than 40K corresponding points among the images, a subset of 25 multi-view stereo images selected from an area of approximately 30 m × 40 m within the scene accompanied with a dense point cloud measured by a terrestrial laser scanner.

show abstract

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Unmanned aerial image dataset: Ready for 3D reconstruction

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…After generating a predefined number of model hypotheses and scoring them based on a subset of the input data, it selects models with high scores for further evaluation. A detailed discussion of standard RANSAC and its variants for image processing and photogrammetry applications can be found in [121] and [122]. In the following paragraphs, we focus only on RANSAC and its variants applicable to geometric shape detection in lidar point clouds.…”

Section: A Shape Primitivesmentioning

confidence: 99%

Geometric Primitives in LiDAR Point Clouds: A Review

Xia

Chen

Wang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

To the best of our knowledge, the most recent light detection and ranging (lidar)-based surveys have been focused only on specific applications such as reconstruction and segmentation, as well as data processing techniques based on a specific platform, e.g., mobile laser. However, in this article, lidar point clouds are understood from a new and universal perspective, i.e., geometric primitives embedded in versatile objects in the physical world. In lidar point clouds, the basic unit is the point coordinate. Geometric primitives that consist of a group of discrete points may be viewed as one kind of abstraction and representation of lidar data at the entity level. We categorize geometric primitives into two classes: shape primitives, e.g., lines, surfaces, and volumetric shapes, and structure primitives, represented by skeletons and edges. In recent years, many efforts from different communities, such as photogrammetry, computer vision, and computer graphics, have been made to finalize geometric primitive detection, regularization, and in-depth applications. Interpretations of geometric primitives from multiple disciplines try to convey the significance of geometric primitives, the latest processing techniques regarding geometric primitives, and their potential possibilities in the context of lidar point clouds. To this end, primitive-based applications are reviewed with an emphasis on object extraction and reconstruction to clearly show the significances of this article. Next, we survey and compare methods for geometric primitive extraction and then review primitive regularization methods that add real-world constrains to initial primitives. Finally, we summarize the challenges, expected applications, and describe possible future for primitive extraction methods that can achieve globally optimal results efficiently, even with disorganized, uneven, noisy, incomplete, and large-scale lidar point clouds.

show abstract

“…Ni et al [19] assumed that the inliers were located close to each other and proposed the so-called GroupSAC algorithm that divides the input data by selecting samples from clusters containing many inliers. A detailed discussion regarding the standard RANSAC algorithm and its variants can be found in [20,21]. Rather than using RANSAC, Awadallah et al [22] projected a point cloud into a two-dimensional grid and obtained a two-dimensional gray-level image based on the mesh density.…”

Section: Introductionmentioning

confidence: 99%

Higher-Order Conditional Random Fields-Based 3D Semantic Labeling of Airborne Laser-Scanning Point Clouds

Chen

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

This paper presents a novel framework to achieve 3D semantic labeling of objects (e.g., trees, buildings, and vehicles) from airborne laser-scanning point clouds. To this end, we propose a framework which consists of hierarchical clustering and higher-order conditional random fields (CRF) labeling. In the hierarchical clustering, the raw point clouds are over-segmented into a set of fine-grained clusters by integrating the point density clustering and the classic K-means clustering algorithm, followed by the proposed probability density clustering algorithm. Through this process, we not only obtain a more uniform size and more homogeneous clusters with semantic consistency, but the topological relationships of the cluster’s neighborhood are implicitly maintained by turning the problem of topology maintenance into a clustering problem based on the proposed probability density clustering algorithm. Subsequently, the fine-grained clusters and their topological context are fed into the CRF labeling step, from which the fine-grained cluster’s semantic labels are learned and determined by solving a multi-label energy minimization formulation, which simultaneously considers the unary, pairwise, and higher-order potentials. Our experiments of classifying urban and residential scenes demonstrate that the proposed approach reaches 88.5% and 86.1% of “m F 1 ” estimated by averaging all classes of the F 1 -scores. We prove that the proposed method outperforms five other state-of-the-art methods. In addition, we demonstrate the effectiveness of the proposed energy terms by using an “ablation study” strategy.

show abstract

Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection

Cited by 10 publications

References 56 publications

Unmanned aerial image dataset: Ready for 3D reconstruction

Unmanned aerial image dataset: Ready for 3D reconstruction

Geometric Primitives in LiDAR Point Clouds: A Review

Higher-Order Conditional Random Fields-Based 3D Semantic Labeling of Airborne Laser-Scanning Point Clouds

Contact Info

Product

Resources

About