Accurate Point Cloud Research Articles

Evaluating the Distance Impact to the Scanning Topographic Object with the 3D Point Cloud Resolution

FARO 3D Terrestrial Laser Scanner (TLS) system was used to evaluate the impact of distance on scanning the topographic, building or structure object due to the lack of knowledge on the impact of the distance toward the resolution of the image produced by the terrestrial laser scanner. There is a lot of high-rise building that need to be monitored from time to time to prevent unwanted catastrophe from accruing in a good plan city. This study aimed to investigate the accuracy of point cloud produced by the laser scanner by scanning the object at a different distance are performed, especially to find a suitable distance for optimum accuracy. The main fieldwork activity involved is the selection of the most appropriate position for setting up the TLS at three different distance which is 30m, 40m and 50m from the building. The chosen position must cover the same area view to make sure the image can be used to compare with each other in term of accuracy. Before scanning the selected side of the building, several targets were placed on the building as it is necessary to tie the building to control points on the ground that have been acquired with Real-Time Kinematic (RTK) Global Positioning System (GPS). The image processing is done by following the flow of the scanning using Scene software to link the target with 3D coordinate and generate a point cloud. The accuracy of the 3D image captured at three different distance generated by the Scene software is compared to evaluate the impact of distances on accuracy. It can be suggested that distance selected for the experiment is reliable for high accuracy measurement in monitoring of structural movement.

Point cloud completion using multiscale feature fusion and cross-regional attention

Raw point clouds obtained from real-world scanning are always incomplete and ununiformly distributed, which would result in structural losses of object shapes and bring about difficulties in further high-level 3D vision tasks. Therefore, a learning-based method called CRA-Net is proposed in this paper to repair partial point clouds and predict complete object shapes. Compared with most existing networks that only leverage global features, CRA-Net successfully utilizes local features to restore clearer details of object shapes with low instability. First, we propose an adaptive neighborhood query method that is able to adjust query centers and radiuses to cover different object shapes and acquire balanced local regions. Second, we build a parallel encoder to extract multiscale features from the input. Third, we design a cross-regional attention module based on graph attention network. It quantifies underlying relationships among all the local features under certain conditions interpreted by global features. Based on such relationships, each conditional local feature vector is able to search across the regions and selectively absorb other local features. Fourth, we design a coarse decoder to collect these cross region features and generate the skeleton of complete point cloud. Finally, we refine the coarse point cloud by comparing it with the input, and up sample it using folding-based layers.Our network is first trained and tested on manually made partial-complete point clouds pairs generated by the scanning process of a virtual LiDAR on eight categories of objects. Then it is tested on real-world point clouds of indoor and outdoor scenes. Compared with existing representative methods, our CRA-Net always restores the most accurate point clouds with the clearest details.

A Strip Adjustment Method of UAV-Borne LiDAR Point Cloud Based on DEM Features for Mountainous Area.

Due to the trajectory error of the low-precision position and orientation system (POS) used in unmanned aerial laser scanning (ULS), discrepancies usually exist between adjacent LiDAR (Light Detection and Ranging) strips. Strip adjustment is an effective way to eliminate these discrepancies. However, it is difficult to apply existing strip adjustment methods in mountainous areas with few artificial objects. Thus, digital elevation model-iterative closest point (DEM-ICP), a pair-wise registration method that takes topography features into account, is proposed in this paper. First, DEM-ICP filters the point clouds to remove the non-ground points. Second, the ground points are interpolated to generate continuous DEMs. Finally, a point-to-plane ICP algorithm is performed to register the adjacent DEMs with the overlapping area. A graph-based optimization is utilized following DEM-ICP to estimate the correction parameters and achieve global consistency between all strips. Experiments were carried out using eight strips collected by ULS in mountainous areas to evaluate the proposed method. The average root-mean-square error (RMSE) of all data was less than 0.4 m after the proposed strip adjustment, which was only 0.015 m higher than the result of manual registration (ground truth). In addition, the plane fitting accuracy of lateral point clouds was improved 4.2-fold, from 1.565 to 0.375 m, demonstrating the robustness and accuracy of the proposed method.

Structure from Motion and Archaeological Excavation: Experiences of the Via Consolare Project in Pompeii

The last decade of advances in Image-Based Modeling (IBM) data acquisition based on Structure from Motion (SfM) have made it possible as never before to record excavated archaeological deposits, historical architectural remains, artifacts, and geographical surroundings in the field. Armed only with digital cameras and low-cost or open-source software, researchers can now produce accurate point clouds of millions of points, capturing archaeological information in high-resolution detail. But what changes will IBM really bring to the standards, requirements, and expectations of practical field methodology for projects operating on shoe-string budgets? Since 2010, the Via Consolare Project, a small archaeological research project from a State level University, has employed an entirely open-source and “free for academic use” IBM pipeline to record a variety of archaeological features in Insula VII 6 and the “Villa delle Colonne a mosaico” in Pompeii. Ranging from surviving architecture, to rubble fill layers, to the interiors of inaccessible cisterns and drains, this work has been carried out in preparation for the eventual coordination of these data into a 3D GIS of all recorded stratigraphy. Rarely were sufficient resources available for dedicated equipment or personnel to be devoted to this task. While practical implementation, even in a low-budget excavation environment, has confirmed that this technology can indeed augment archaeological field documentation and provide investigation opportunities that would otherwise be impossible, it failed to replace traditional handdrafted recording techniques and was found to present significant challenges and a number of hidden costs. This emphasizes a need for appropriate and cautious planning in implementation, especially in projects with limited means.

Evaluating the Structure from Motion Technique for Measurement of Bed Morphology in Physical Model Studies

The selection of instrumentation for data acquisition in physical model studies depends on type and resolution of data to be recorded, time frame of the model study, available instrumentation alternatives, availability of skilled personnel and overall budget of the model study. The available instrumentation for recording bed levels or three-dimensional information on geometry of a physical model range from simple manual gauges to sophisticated laser or acoustic sensors. In this study, Structure from Motion (SfM) technique was applied, on three physical model studies of different scales and study objectives, as a cheap, quicker, easy to use and satisfactorily precise alternative for recording 3D point data in form of colour coded dense point cloud representing the model geometry especially the river bed levels in the model. The accuracy of 3D point cloud generated with SfM technique were also assessed by comparing with data obtained from manual measurement using conventional surveying technique in the models and the results were found to be very promising.

Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimation

Stereo-LiDAR fusion is a promising task in that we can utilize two different types of 3D perceptions for practical usage - dense 3D information (stereo cameras) and highly-accurate sparse point clouds (LiDAR). However, due to their different modalities and structures, the method of aligning sensor data is the key for successful sensor fusion. To this end, we propose a geometry-aware stereo-LiDAR fusion network for long-range depth estimation, called volumetric propagation network. The key idea of our network is to exploit sparse and accurate point clouds as a cue for guiding correspondences of stereo images in a unified 3D volume space. Unlike existing fusion strategies, we directly embed point clouds into the volume, which enables us to propagate valid information into nearby voxels in the volume, and to reduce the uncertainty of correspondences. Thus, it allows us to fuse two different input modalities seamlessly and regress a long-range depth map. Our fusion is further enhanced by a newly proposed feature extraction layer for point clouds guided by images: FusionConv. FusionConv extracts point cloud features that consider both semantic (2D image domain) and geometric (3D domain) relations and aid fusion at the volume. Our network achieves state-of-the-art performance on KITTI and Virtual-KITTI datasets among recent stereo-LiDAR fusion methods.

3D Reconstruction of Coastal Cliffs from Fixed-Wing and Multi-Rotor UAS: Impact of SfM-MVS Processing Parameters, Image Redundancy and Acquisition Geometry

Monitoring the dynamics of coastal cliffs is fundamental for the safety of communities, buildings, utilities, and infrastructures located near the coastline. Structure-from-Motion and Multi View Stereo (SfM-MVS) photogrammetry based on Unmanned Aerial Systems (UAS) is a flexible and cost-effective surveying technique for generating a dense 3D point cloud of the whole cliff face (from bottom to top), with high spatial and temporal resolution. In this paper, in order to generate a reproducible, reliable, precise, accurate, and dense point cloud of the cliff face, a comprehensive analysis of the SfM-MVS processing parameters, image redundancy and acquisition geometry was performed. Using two different UAS, a fixed-wing and a multi-rotor, two flight missions were executed with the aim of reconstructing the geometry of an almost vertical cliff located at the central Portuguese coast. The results indicated that optimizing the processing parameters of Agisoft Metashape can improve the 3D accuracy of the point cloud up to 2 cm. Regarding the image acquisition geometry, the high off-nadir (90°) dataset taken by the multi-rotor generated a denser and more accurate point cloud, with lesser data gaps, than that generated by the low off-nadir dataset (3°) taken by the fixed wing. Yet, it was found that reducing properly the high overlap of the image dataset acquired by the multi-rotor drone permits to get an optimal image dataset, allowing to speed up the processing time without compromising the accuracy and density of the generated point cloud. The analysis and results presented in this paper improve the knowledge required for the 3D reconstruction of coastal cliffs by UAS, providing new insights into the technical aspects needed for optimizing the monitoring surveys.

A Robust Deconvolution Method of Airborne LiDAR Waveforms for Dense Point Clouds Generation in Forest

The generation of dense and accurate point clouds from airborne light detection and ranging (LiDAR) waveform data is crucial to forest inventory. This work proposes a deconvolution method with: 1) an automatic stopping criterion to differentiate near-adjacent targets and 2) an iterative false subwaveform removal algorithm to remove outliers caused by noise. Synthetic waveforms with different overlap rates were processed using the proposed method, the Gaussian decomposition (GD) method, and the Richardson Lucy (RL) deconvolution method. Results showed that: 1) the number of subwaveforms detected by the proposed method is 9% higher than that of the RL and 20% higher than that of the GD when the overlap rates are larger than 0.6 and 2) the proposed method is of the smallest ground and peak distance errors. Results of the indoor experiment also show that the proposed method is superior in finding near targets meanwhile leading to small ground and peak distance error. Furthermore, the proposed method was tested by airborne waveforms from the Dagujia forest farm. The point cloud density acquired by the proposed method is 3% and 35% higher than that by the RL and GD method. Fewer outliers are produced by the proposed method. The number of individual trees extracted from the proposed point clouds is 22%, 51%, and 57% greater than those extracted from the RL, the GD, and the reference point clouds using the canopy height model-based method. Best individual tree extraction result is produced by the proposed method, especially for an area with small trees.

Quality control and crop characterization framework for multi-temporal UAV LiDAR data over mechanized agricultural fields

Recent developments in remote sensing are enabling automatic, high resolution, and non-destructive survey of agriculture fields, providing the key basis for advancing plant breeding. Among the used remote sensing modalities, LiDAR has attracted wide attention for its ability to directly provide accurate 3D information. Despite the increasing utilization of LiDAR technology in phenotyping, there is still a lack of effective quality control strategies, in particular, quality control of LiDAR data collected on a multi-temporal basis. This study proposes a targetless framework for multi-temporal LiDAR data quality control and crop characterization in mechanized agricultural fields. Features extracted from the fields – terrain patches and row/alley locations – are utilized for evaluating the vertical and planimetric relative accuracy of the point clouds. Row/alley locations in the field are automatically identified from the point clouds based on the assumption that higher point density and/or higher elevation correspond to plant locations. The performance of the proposed quality control strategies is evaluated using multi-temporal datasets collected in agricultural fields of different sizes, orientation, crops, and growth stages. The result shows that the net vertical and planimetric discrepancies between multi-temporal point clouds are ±3 cm and ±8 cm, respectively. While the former reflects the actual accuracy of the point clouds, the latter is a combined effect of the LiDAR point cloud accuracy, rasterization artifacts, crop type, growth pattern, and wind condition during data acquisition. In terms of row and alley detection, the result shows that the proposed strategy achieves high performance and can deal with different planting orientation, crop types, growth stages, canopy cover, and planting density. In conclusion, this study presents a quality control framework for multi-temporal LiDAR data. Moreover, the row and alley detection leads to automated extraction of plots, and hence facilitates the use of remotely sensed data for automated phenotyping.

Automated shape and pose updating of building information model elements from 3D point clouds

Generating an up-to-date BIM that accurately reflects as-built conditions is becoming necessary for ensuring fit between assemblies and construction sites. If an initial BIM (“proto-BIM”) could be automatically updated to reflect as-built conditions by changing shape and pose of BIM elements, it would be preferable over scan-to-BIM in many instances. An approach is presented herein for creating geometric agency within BIMs by exploiting their parametric capabilities, the accuracy of 3D point clouds, and the dexterity of metaheuristics. The result is a dynamic BIM (“dyna-BIM”) capable of updating its geometry to match a 3D point cloud. A case study for cast-in-place concrete footings shows how the average error between a BIM and the as-built conditions can be reduced from 50.4 mm to 5.69 mm. This paper provides a way to progressively capture accurate as-built conditions in BIM and predict potential assembly conflicts, while maintaining the initial semantics and fidelity of an as-designed BIM.

Airborne LiDAR point cloud classification with global-local graph attention convolution neural network

Airborne light detection and ranging (LiDAR) plays an increasingly significant role in urban planning, topographic mapping, environmental monitoring, power line detection and other fields thanks to its capability to quickly acquire large-scale and high-precision ground information. To achieve point cloud classification, previous studies proposed point cloud deep learning models that can directly process raw point clouds based on PointNet-like architectures. And some recent works proposed graph convolution neural network based on the inherent topology of point clouds. However, the above point cloud deep learning models only pay attention to exploring local geometric structures, yet ignore global contextual relationships among all points. In this paper, we present a global-local graph attention convolution neural network (GACNN) that can be directly applied to the classification of unstructured 3D point clouds obtained by airborne LiDAR. Specifically, we first introduce a graph attention convolution module that incorporates global contextual information and local structural features. The global attention module examines spatial relationships among all points, while the local attention module can dynamically learn convolution weights with regard to the spatial position of the local neighboring points and reweight the convolution weights by inspecting the density of each local region. Based on the proposed graph attention convolution module, we further design an end-to-end encoder-decoder network, named GACNN, to capture multiscale features of the point clouds and therefore enable more accurate airborne point cloud classification. Experiments on the ISPRS 3D labeling dataset show that the proposed model achieves a new state-of-the-art performance in terms of average F1 score (71.5%) and a satisfying overall accuracy (83.2%). Additionally, experiments further conducted on the 2019 Data Fusion Contest Dataset by comparing with other prevalent point cloud deep learning models demonstrate the favorable generalization capability of the proposed model.

Kinematic In Situ Self-Calibration of a Backpack-Based Multi-Beam LiDAR System

Light Detection and Ranging (LiDAR) remote sensing technology provides a more efficient means to acquire accurate 3D information from large-scale environments. Among the variety of LiDAR sensors, Multi-Beam LiDAR (MBL) sensors are one of the most extensively applied scanner types for mobile applications. Despite the efficiency of these sensors, their observation accuracy is relatively low for effective use in mobile mapping applications, which require measurements at a higher level of accuracy. In addition, measurement instability of MBL demonstrates that frequent re-calibration is necessary to maintain a high level of accuracy. Therefore, frequent in situ calibration prior to data acquisition is an essential step in order to meet the accuracy-level requirements and to implement these scanners for precise mobile applications. In this study, kinematic in situ self-calibration of a backpack-based MBL system was investigated to develop an accurate backpack-based mobile mapping system. First, simulated datasets were generated for the experiments and tested in a controlled environment to inspect the minimum network configuration for self-calibration. For this purpose, our own-developed simulator program was first utilized to generate simulation datasets with various observation settings, network configurations, test sites, and targets. Afterwards, self-calibration was carried out using the simulation datasets. Second, real datasets were captured in a kinematic situation so as to compare the calibration results with the simulation experiments. The results demonstrate that the kinematic self-calibration of the backpack-based MBL system could improve the point cloud accuracy with Root Mean Square Error (RMSE) of planar misclosure up to 81%. Conclusively, in situ self-calibration of the backpack-based MBL system can be performed using on-site datasets, reaching the higher accuracy of point cloud. In addition, this method, by performing automatic calibration using the scan data, has the potential to be adapted to on-line re-calibration.

Application of fusion 2D lidar and binocular vision in robot locating obstacles

If robot uses 2D lidar or binocular camera to locate obstacles, there will be some problems such as missing obstacle information or inaccurate obstacle locating, which will affect the normal work of the robot. In order to obtain accurate 3D obstacle information, this paper proposes an algorithm for fusing 2D lidar and binocular vision to complete the obstacle location. In this paper, the depth value of the 2D lidar point cloud is used as a benchmark. By fitting the error equation of the binocular camera point cloud depth value, the depth value of the camera point cloud is modified to obtain an accurate 3D camera point cloud, thereby obtaining an accurate 3D obstacle information. Many experiments have proved that the fusion algorithm of 2D lidar and binocular vision can obtain accurate 3D obstacle information. The method of fusion 2D lidar and binocular vision can approximately achieve the measurement effect of 3D lidar, and the point cloud of obstacles is relatively dense, so the accurate 3D obstacle information can be obtained. This method can reduce the influence of single sensor on the robot locating obstacles, thus completing the accurate locating of obstacles, which is of certain significance to robot navigation.

Accurate Virtual Trial Assembly Method of Prefabricated Steel Components Using Terrestrial Laser Scanning

The comprehensive utilization of prefabricated components (PCs) is one of the features of industrial construction. Trial assembly is imperative for PCs used in high‐rise buildings and large bridges. Virtual trial assembly (VTA) is a preassembly process for PCs in a virtual environment that can avoid the time‐consuming and economic challenges in physical trial assembly. In this study, a general framework for VTA that is performed between a point cloud, a building information model (BIM), and the finite element method is proposed. In obtaining point clouds via terrestrial laser scanning, the registration accuracy of point clouds is the key to building an accurate digital model of PCs. Accordingly, an accurate registration method based on triangular pyramid markers is proposed. This method can enable the general registration accuracy of point clouds to reach the submillimeter scale. Two algorithms for curved members and bolt holes are developed for PCs with bolt assembly to reconstruct a precise BIM that can be used directly in finite element analysis. Furthermore, an efficient simulation method for accurately predicting the elastic deformation and initial stress caused by forced assembly is proposed and verified. The proposed VTA method is verified on a reduced‐scale steel pipe arch bridge. Experimental results show that the geometric prediction deviation of VTA is less than 1/1800 of the experimental bridge span, and the mean stress predicted via VTA is 90% of the measured mean stress. In general, this research may help improve the industrialization level of building construction.

A Hierarchical Approach for Point Cloud Classification With 3D Contextual Features

Classifying point cloud of urban landscapes plays essential roles in many urban applications. However, automating such a task is challenging due to irregular point distribution and complex urban scenes. Incorporating contextual information is crucial in improving classification accuracy of point clouds. In this article, we propose a hierarchical approach for point cloud classification with 3-D contextual features, which comprises three steps:segment-based classification with primitive features and a random forest classifier; extracting novel 3-D contextual features from the initial labels considering spatial relationships between neighboring segments and semantic dependencies; and refining classification with a combination of primitive features and spatial contextual features, and a hierarchical multilayer perceptron classifier that considers primitive features and spatial contextual features at different levels. The proposed method was tested on two point cloud datasets:the National University of Singapore (NUS) dataset and the Vaihingen benchmark dataset of the International Society of Photogrammetry and Remote Sensing. The evaluation results showed that the proposed method achieved an overall accuracy of 92.51% and 82.34% for the NUS dataset and Vaihingen dataset, respectively. The feature importance evaluation showed that 3-D spatial contextual features contributed useful information for discriminating different classes, such as roof, facade, grassland, tree, and ground. Quantitative comparisons further showed that the proposed method is more advantageous, especially in the detection of class roof and facade.

3D-LIDAR Based Object Detection and Tracking on the Edge of IoT for Railway Level Crossing

Object detection is an essential technology for surveillance systems, particularly in areas with a high risk of accidents such as railway level crossings. To prevent future collisions, the system must detect and track any object that passes through the monitored area with high accuracy, and this process must be performed fulfilling real-time specifications. In this work, an edge IoT HW platform implementation capable of detecting and tracking objects in a railway level crossing scenario is proposed. The response of the system has to be calculated and sent from the proposed IoT platform to the train, so as to trigger a warning action to avoid a possible collision. The system uses a low-resolution 3D 16-channel LIDAR as a sensor that provides an accurate point cloud map with a large amount of data. The element used to process the information is a custom embedded edge platform with low computing resources and low-power consumption. This processing element is located as close as possible to the sensor, where data is generated to improve latency, privacy, and avoid bandwidth limitations, compared to performing processing in the cloud. Additionally, lightweight object detection and tracking algorithm is proposed in this work to process a large amount of information provided by the LIDAR, allowing to reach real-time specifications. The proposed method is validated quantitatively by carrying out implementation on a car road, emulating a railway level crossing.

Accurate and dense point cloud generation for industrial Measurement via target-free photogrammetry

Industrial photogrammetry systems commonly require multiple coded targets to establish a global coordinate frame for relatively large objects. However, there are many industrial products that do not allow coded targets to be placed on their surfaces. We propose an accurate and dense point cloud generation approach for measuring large-sized, untextured objects. Unlike most existing industrial measurement methods, our photogrammetry approach is free of coded targets. We have three core contributions. First, a Rotation-Free Digital Image Correlation (RFDIC) method is proposed to improve the multi-view stereopsis matching precision. Second, based on the technique of structure from motion (SfM), a coarse-to-fine strategy is utilized to construct the multi-view geometry and accurately optimize camera poses. Third, the devices used are consumer-friendly and commonly available, i.e., only a digital projector and a camera are needed to obtain the dense points on the measured object surface. The experimental results show that the error of reconstructed length for a scale bar is less than 0.01 mm/m. Compared to the state-of-the-art commercial measurement system, the average error of point cloud reconstruction is about 0.055 mm, which meets the accuracy demand for industrial applications.

Structural Elements Detection and Reconstruction (SEDR): A Hybrid Approach for Modeling Complex Indoor Structures

We present a hybrid approach for modeling complex interior structural elements from the unstructured point cloud without additional information. The proposed approach focuses on an integrated modeling strategy that can reconstruct structural elements and keep the balance of model completeness and quality. First, a data-driven approach detects the complete structure points of indoor scenarios including the curved wall structures and detailed structures. After applying the down-sampling process to point cloud dataset, ceiling and floor points are detected by RANSAC. The ceiling boundary points are selected as seed points of the growing algorithm to acquire points related to the wall segments. Detailed structures points are detected using the Grid-Slices analysis approach. Second, a model-driven refinement is conducted to the structure points that aims to decrease the impact of point cloud accuracy on the quality of the model. RANSAC algorithm is implemented to detect more accurate layout, and the hole in structure points is repaired in this refinement step. Lastly, the Screened Poisson surface reconstruction approach is conducted to generate the model based on the structure points after refinement. Our approach was validated on the backpack laser dataset, handheld laser dataset, and synthetic dataset, and experimental results demonstrate that our approach can preserve the curved wall structures and detailed structures in the model with high accuracy.

Local Feature Performance Evaluation for Structure-From-Motion and Multi-View Stereo Using Simulated City-Scale Aerial Imagery

Ubiquitous low cost multi-rotor and fixed wing drones or unmanned aerial vehicles (UAVs) have accelerated the need for reliable, robust, and scalable Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipelines suitable for a variety of flightpath trajectories especially in degraded environments. Feature tracking being a core part of SfM and MVS, is essential for multiview scene modeling and perception, but difficult to evaluate in large scale datasets due to the lack of sufficient ground-truth. For large-scale aerial imagery, accurate camera orientation and dense 3D point cloud accuracy can be used to assess the impact of accurate feature localization and track length. We propose a novel view simulation (or synthesis) framework which generates visually realistic new unseen camera views for feature detection using known high fidelity camera poses for modeling. Seven state-of-the-art local handcrafted and learning-based features are quantitatively evaluated for robustness and matchability within the SfM and MVS pipelines using the open source COLMAP software. Our experimental results provide performance rankings of each feature, using twelve different evaluation metrics across three synthetic city-wide aerial image sequences. We show that recent learned features, SuperPoint and LF-Net, have not only reached the quality of the best handcrafted features like SIFT and SURF, but now outperform them in terms of more accurate 3D camera pose estimates and longer feature tracks. SuperPoint produces 1.51 meter average position error and 0.03° average angular error, while SIFT remains competitive (second best for pose and overall) with 1.78 meter and 0.11° errors respectively.

Suitability Study of Structure-from-Motion for the Digitisation of Architectural (Heritage) Spaces to Apply Divergent Photograph Collection

The digitisation of architectural heritage has experienced a great development of low-cost and high-definition data capture technologies, thus enabling the accurate and effective modelling of complex heritage assets. Accordingly, research has identified the best methods to survey historic buildings, but the suitability of Structure-from-Motion/Multi-view-Stereo (SfM/MVS) for interior square symmetrical architectural spaces is unexplored. In contrast to the traditional SfM surveying for which the camera surrounds the object, the photograph collection approach is divergent in courtyards. This paper evaluates the accuracy of SfM point clouds against Terrestrial Laser Scanning (TLS) for these large architectural spaces with a symmetrical configuration, with the main courtyard of Casa de Pilatos in Seville, Spain, as a case study. Two different SfM surveys were conducted: (1) Without control points, and (2) referenced using a total station. The first survey yielded unacceptable results: A standard deviation of 0.0576 m was achieved in the northwest sector of the case study, mainly because of the difficulty of aligning the SfM and TLS data due to the way they are produced. This value could be admissible depending on the purpose of the photogrammetric model.