CRBeDaSet: A Benchmark Dataset for High Accuracy Close Range 3D Object Reconstruction

Abstract

This paper presents the CRBeDaSet—a new benchmark dataset designed for evaluating close range, image-based 3D modeling and reconstruction techniques, and the first empirical experiences of its use. The test object is a medium-sized building. Diverse textures characterize the surface of elevations. The dataset contains: the geodetic spatial control network (12 stabilized ground points determined using iterative multi-observation parametric adjustment) and the photogrammetric network (32 artificial signalized and 18 defined natural control points), measured using Leica TS30 total station and 36 terrestrial, mainly convergent photos, acquired from elevated camera standpoints with non-metric digital single-lens reflex Nikon D5100 camera (ground sample distance approx. 3 mm), the complex results of the bundle block adjustment with simultaneous camera calibration performed in the Pictran software package, and the colored point clouds (ca. 250 million points) from terrestrial laser scanning acquired using the Leica ScanStation C10 and post-processed in the Leica Cyclone™ SCAN software (ver. 2022.1.1) which were denoized, filtered, and classified using LoD3 standard (ca. 62 million points). The existing datasets and benchmarks were also described and evaluated in the paper. The proposed photogrammetric dataset was experimentally tested in the open-source application GRAPHOS and the commercial suites ContextCapture, Metashape, PhotoScan, Pix4Dmapper, and RealityCapture. As the first experience in its evaluation, the difficulties and errors that occurred in the software used during dataset digital processing were shown and discussed. The proposed CRBeDaSet benchmark dataset allows obtaining high accuracy (“mm” range) of the photogrammetric 3D object reconstruction in close range, based on a multi-image view uncalibrated imagery, dense image matching techniques, and generated dense point clouds.

Contactless Size Reference in Forensic Photography—Design and Verification of the Novel FreeRef-1 System

“…Furthermore, next to requiring large amounts of data to store LiDAR data with a full set of photogrammetry images, its absolute accuracy for small objects > 10 cm is only about 1 cm (using an iPhone) [7], which is up to 10 times worse than the FreeRef-1 system, depending on the distance and projection plane angles. When using specialized LiDAR equipment, the accuracies of point locations can be in the sub-centimeter range, but besides being still insufficiently accurate for small dimensions, these devices are bulky and expensive and require expert operators [8][9][10].…”

Key-Point-Descriptor-Based Image Quality Evaluation in Photogrammetry Workflows

“…Photogrammetry offers a sophisticated method to extract precise geometric information from photographs. This technology enables the creation of detailed three-dimensional (3D) models of objects [1][2][3][4] or scenes [5][6][7][8], transforming the way industries and sciences approach visualization and analysis [9][10][11][12][13][14][15]. Utilization of photogrammetry spans a diverse range of fields, underlining its significance in contemporary applications that demand accuracy and detail [16][17][18][19][20][21][22].…”

“…Values in the table are the percentages of selections that are correct. The first five methods(1)(2)(3)(4)(5) are the baseline image quality evaluation methods. The rest of the methods(6)(7)(8)(9)(10)(11)(12)(13)(14) are proposed key-point-descriptor-based image quality evaluation methods.…”