This research attempted to determine the optimal photo overlap, number of control points and method of camera calibration for a photogrammetric 3D model reconstruction of an object of cultural heritage value. Terrestrial images of the object were taken with a hand‑held digital camera and processed in the ContextCapture software using the Structure‑from‑Motion (SfM) algorithm. A total station was used to measure ground control points (GCPs) and check points. Here, the research workflow, methodology, and various analyses concerning different configurations of the aforementioned factors are described. An attempt to assess the parameters which should be implemented in order to provide a high degree of accuracy of the model and reduce time‑consumption both during fieldwork and data processing was taken. The manuscript discusses the results of the analyses and compares them with other studies presented by different authors and indicates further potential directions of studies within this scope. Based on the authors’ experience with this research, some general conclusions and remarks concerning the planning of photo acquisition from the terrestrial level for the purpose of 3D model reconstruction were formulated.

Thermal infrared imagery is very much gaining in importance in the diagnosis of energy losses in cultural heritage through non-destructive measurement methods. Hence, owing to the fact that it is a very innovative and, above all, safe solution, it is possible to determine the condition of the building, locate places exposed to thermal escape, and plan actions to improve the condition of the facility. The presented work is devoted to the technology of creating a dense point cloud and a 3D model, based on data obtained from UAV. It has been shown that it is possible to build a 3D point model based on thermograms with the specified accuracy by using thermal measurement marks and the dense matching method. The results achieved in this way were compared and, as the result of this work, the model obtained from color photos was integrated with the point cloud created on the basis of the thermal images. The discussed approach exploits measurement data obtained with three independent devices (tools/appliances): a Matrice 300 RTK drone (courtesy of NaviGate); a Phantom 4 PRO drone; and a KT-165 thermal imaging camera. A stone church located in the southern part of Poland was chosen as the measuring object.

The altimetric accuracy of aerial laser scanning (ALS) data is one of the most important issues of ALS data processing. In this paper, the authors present a previously unknown, yet simple and efficient method for altimetric enhancement of ALS data based on the concept of lidargrammetry. The generally known photogrammetric theory of stereo model deformations caused by relative orientation parameters errors of stereopair was applied for the continuous correction of lidar data based on ground control points. The preliminary findings suggest that the method is correct, efficient and precise, whilst the correction of the point cloud is continuous. The theory of the method and its implementation within the research software are presented in the text. Several tests were performed on synthetic and real data. The most significant results are presented and discussed in the article together with a discussion of the potential of lidargrammetry, and the main directions of future research are also mapped out. These results confirm that the research gap in the area of altimetric enhancement of ALS data without additional trajectory data is resolved in this study.

The accuracy of spatial raw data is contingent on a number of factors, including the accuracy of the sensors, their calibration, and direct and indirect referencing. The efficacy of the captured data can be enhanced through the implementation of effective data processing methodologies. The present research is dedicated to the enhancement of combined photogrammetric and LiDAR data.In the contemporary context, the trajectory of the vehicle can be affected by GPS signal jamming, a phenomenon that is attributable to international circumstances. This necessitates a combined adjustment of RGB/CIR and LiDAR data. The proposed method necessitates the utilisation of both sets of data: The study area was imaged using both RGB and CIR technologies, with intensity LiDAR strips also employed to cover the same area. The present approach is founded upon the notion of lidargrammetry (Jayendra-Lakshman and Devarajan, 2013), which uses photogrammetric algorithms for lidar data processing (Rzonca and Twardowski, 2022).The research tool, PyLiGram, generates synthetic images of the LiDAR data, known as lidargrams or renders, applying the same interior orientation parameters (IOPs) as the real camera used to capture the real RGB/CIR images. The synthetic images are centrally projected according to IOPs and predefined external orientation parameters (EOPs). The projection process is reversible by use of unique lidar point identifiers. Points can be projected onto synthetic images, and after processing the same images, points can be intersected to create a new point cloud.The paper sets out the potential of applying photogrammetric methods to the co-adjustment of RGB/CIR and LiDAR data. The process entails the collaborative processing of RGB/CIR and LiDAR images through the utilisation of deep learning matching techniques and common least squares adjustment. The refined EOPs are then utilised to intersect the refined point cloud and enhance the images' positions.A key benefit of this approach is that it has the potential to eliminate the necessity for LiDAR control patches/points or ground control points for RGB/CIR images, depending on the specific case.The process was subjected to a series of trials on multiple datasets, each comprising a point cloud density of approximately 100 points per square metre and images with a ground sample distance (GSD) ranging from 5 to 10 centimetres.

The present paper introduces a novel methodology for LiDAR point transformation and adjustment, grounded in two primary concepts. In the initial phase of the process, LiDAR data are mapped onto synthetic images, known as lidargrams, through the utilization of exterior orientation parameters (EOPs) of a virtual camera. Secondly, unique lidargram point identifiers (ULPIs) are assigned to each LiDAR point, ensuring the preservation of the relationship between specific LiDAR points and their corresponding lidargram projections. This process facilitates the reconstruction of ground points from their respective projections. The integration of these concepts facilitates the alignment and adjustment of blocks of lidargrams, thereby enabling the estimation of novel EOPs. The exchange of arbitrary EOPs and the intersection of the transformed point cloud based on the ULPIs are facilitated by these refined EOPs. The LiDAR data undergo a three-dimensional transformation using photogrammetric algorithms. This is in accordance with the fundamental principles of lidargrammetry. The accuracy of the new approach and its implementation in a research tool were verified on a range of data types, encompassing synthetic, semisynthetic, and real data. By evaluating the approach across a wide range of data sources, the authors were able to assess its effectiveness and reliability in different scenarios. The method’s flexibility is evidenced by its ability to reduce the final 3D root mean square error of discrepancies measured at check points by 30 times in synthetic data tests, 12 times in semisynthetic data tests, and 96 times in real data tests. The quantitative results obtained provide substantial support for the validity of the presented methodology. The efficacy of the proposed method was also evaluated by way of a comparative analysis with a selection of widely utilized LiDAR processing software developed by TerraSolid Ltd.