Evaluation of the effectiveness of HDR tone-mapping operators for photogrammetric applications

[EN] The ability of High Dynamic Range (HDR) imaging to capture the full range of lighting in a scene has meant that it is being increasingly used for Cultural Heritage (CH) applications. Photogrammetric techniques allow the semi-automatic production of 3D models from a sequence of images. Current photogrammetric methods are not always effective in reconstructing images under harsh lighting conditions, as significant geometric details may not have been captured accurately within under- and over-exposed regions of the image. HDR imaging offers the possibility to overcome this limitation, however the HDR images need to be tone mapped before they can be used within existing photogrammetric algorithms. In this paper we evaluate four different HDR tone-mapping operators (TMOs) that have been used to convert raw HDR images into a format suitable for state-of-the-art algorithms, and in particular keypoint detection techniques. The evaluation criteria used are the number of keypoints, the number of valid matches achieved and the repeatability rate. The comparison considers two local and two global TMOs. HDR data from four CH sites were used: Kaisariani Monastery (Greece), Asinou Church (Cyprus), Château des Baux (France) and Buonconsiglio Castle (Italy).We would like to thank Kurt Debattista, Timothy Bradley, Ratnajit Mukherjee, Diego Bellido Castañeda and TomBashford Rogers for their suggestions, help and encouragement. We would like to thank the hosting institutions: 3D Optical Metrology Group, FBK (Trento, Italy) and UMR 3495 MAP CNRS/MCC (Marseille, France), for their support during the data acquisition campaigns. This project has received funding from the European Union’s 7 th Framework Programme for research, technological development and demonstration under grant agreement No. 608013, titled “ITN-DCH: Initial Training Network for Digital Cultural Heritage: Projecting our Past to the Future”.Suma, R.; Stavropoulou, G.; Stathopoulou, EK.; Van Gool, L.; Georgopoulos, A.; Chalmers, A. (2016). Evaluation of the effectiveness of HDR tone-mapping operators for photogrammetric applications. Virtual Archaeology Review. 7(15):54-66. https://doi.org/10.4995/var.2016.6319SWORD546671

Automatic Alignment of 3D Multi-Sensor Point Clouds

Automatic 3D point cloud alignment is a major research topic in photogrammetry, computer vision and computer graphics. In this research, two keypoint feature matching approaches have been developed and proposed for the automatic alignment of 3D point clouds, which have been acquired from different sensor platforms and are in different 3D conformal coordinate systems. The first proposed approach is based on 3D keypoint feature matching. First, surface curvature information is utilized for scale-invariant 3D keypoint extraction. Adaptive non-maxima suppression (ANMS) is then applied to retain the most distinct and well-distributed set of keypoints. Afterwards, every keypoint is characterized by a scale, rotation and translation invariant 3D surface descriptor, called the radial geodesic distance-slope histogram. Similar keypoints descriptors on the source and target datasets are then matched using bipartite graph matching, followed by a modified-RANSAC for outlier removal. The second proposed method is based on 2D keypoint matching performed on height map images of the 3D point clouds. Height map images are generated by projecting the 3D point clouds onto a planimetric plane. Afterwards, a multi-scale wavelet 2D keypoint detector with ANMS is proposed to extract keypoints on the height maps. Then, a scale, rotation and translation-invariant 2D descriptor referred to as the Gabor, Log-Polar-Rapid Transform descriptor is computed for all keypoints. Finally, source and target height map keypoint correspondences are determined using a bi-directional nearest neighbour matching, together with the modified-RANSAC for outlier removal. Each method is assessed on multi-sensor, urban and non-urban 3D point cloud datasets. Results show that unlike the 3D-based method, the height map-based approach is able to align source and target datasets with differences in point density, point distribution and missing point data. Findings also show that the 3D-based method obtained lower transformation errors and a greater number of correspondences when the source and target have similar point characteristics. The 3D-based approach attained absolute mean alignment differences in the range of 0.23m to 2.81m, whereas the height map approach had a range from 0.17m to 1.21m. These differences meet the proximity requirements of the data characteristics and the further application of fine co-registration approaches

HarrisZ+^+: Harris Corner Selection for Next-Gen Image Matching Pipelines

Due to its role in many computer vision tasks, image matching has been subjected to an active investigation by researchers, which has lead to better and more discriminant feature descriptors and to more robust matching strategies, also thanks to the advent of the deep learning and the increased computational power of the modern hardware. Despite of these achievements, the keypoint extraction process at the base of the image matching pipeline has not seen equivalent progresses. This paper presents HarrisZ$^+$, an upgrade to the HarrisZ corner detector, optimized to synergically take advance of the recent improvements of the other steps of the image matching pipeline. HarrisZ$^+$ does not only consists of a tuning of the setup parameters, but introduces further refinements to the selection criteria delineated by HarrisZ, so providing more, yet discriminative, keypoints, which are better distributed on the image and with higher localization accuracy. The image matching pipeline including HarrisZ$^+$, together with the other modern components, obtained in different recent matching benchmarks state-of-the-art results among the classic image matching pipelines. These results are quite close to those obtained by the more recent fully deep end-to-end trainable approaches and show that there is still a proper margin of improvement that can be granted by the research in classic image matching methods

Robust Key-Frame Stereo Visual SLAM with low-threshold Point and Line Features

In this paper, we develop a robust, efficient visual SLAM system that utilizes spatial inhibition of low threshold, baseline lines, and closed-loop keyframe features. Using ORB-SLAM2, our methods include stereo matching, frame tracking, local bundle adjustment, and line and point global bundle adjustment. In particular, we contribute re-projection in line with the baseline. Fusing lines in the system consume colossal time, and we reduce the time from distributing points to utilizing spatial suppression of feature points. In addition, low threshold key points can be more effective in dealing with low textures. In order to overcome Tracking keyframe redundant problems, an efficient and robust closed-loop tracking key frame is proposed. The proposed SLAM has been extensively tested in KITTI and EuRoC datasets, demonstrating that the proposed system is superior to state-of-the-art methods in various scenarios.Comment: 8 pages, 14 figure