Self-calibrating Bundle Adjustment Research Articles

Simultaneous Calibration of Multiple Cameras and Generation of Omnidirectional Images

Abstract. Omnidirectional images are increasingly being used in various areas, such as urban mapping, virtual reality, agriculture, and robotics. These images can be generated by different acquisition systems, including multi-camera systems, which can acquire higher-resolution images. Stitching techniques are often used and can be suitable for non-metric applications, but rigorous photogrammetric processing is recommended when having more accurate requirements. The main challenges related to this kind of product are the system calibration and the generation of the final omnidirectional images. When using multi-camera systems, the displacement of the cameras' perspective centres can affect the generation of the omnidirectional images and the resulting accuracy. A common approach to minimising the resulting parallax error is to establish a value for the projection cylinder radius as close as possible to the object's depth. This work proposes a highly accurate simultaneous calibration technique for multiple camera systems using self-calibrating bundle adjustment with constraints of stability of the relative orientation parameters. These parameters are later used to generate a projecting cylindrical surface, maintaining the original camera perspective centres and relative orientation angles. The experiments show that using constraints improved both the calibration results and the final omnidirectional images. Residual mismatches between points in overlapping areas are subpixel.

Enhancing Single-Plane Fluoroscopy: A Self-Calibrating Bundle Adjustment for Distortion Modeling.

Single-plane fluoroscopy systems with image intensifiers remain commonly employed in a clinical setting. The imagery they capture is vulnerable to several types of geometric distortions introduced by the system's components and their assembly as well as interactions with the local and global magnetic fields. In this study, the application of a self-calibrating bundle adjustment is investigated as a method to correct geometric distortions in single-plane fluoroscopic imaging systems. The resulting calibrated imagery is then applied in the quantitative analysis of diaphragmatic motion and potential diagnostic applications to hemidiaphragm paralysis. The calibrated imagery is further explored and discussed in its potential impact on areas of surgical navigation. This work was accomplished through the application of a controlled experiment with three separate Philips Easy Diagnost R/F Systems. A highly redundant (~2500 to 3500 degrees-of-freedom) and geometrically strong network of 18 to 22 images of a low-cost target field was collected. The target field comprised 121 pre-surveyed tantalum beads embedded on a 25.4 mm × 25.4 mm acrylic base plate. The modeling process resulted in the estimation of five to eight distortion coefficients, depending on the system. The addition of these terms resulted in 83-85% improvement in terms of image point precision (model fit) and 85-95% improvement in 3D object reconstruction accuracy after calibration. This study demonstrates significant potential in enhancing the accuracy and reliability of fluoroscopic imaging, thereby improving the overall quality and effectiveness of medical diagnostics and treatments.

Analysis of PlanetScope Dove Digital Surface Model Accuracy Using Geometrically Simulated Images

Many objectives in geoscience and engineering require Earth surface elevations at greater temporospatial resolution and coverage than are currently available. This may be achieved with stereo imagery from large constellations of “small sats”, such as PlanetScope Doves. Obtaining Digital Surface Models (DSMs) of sufficient quality from these images is challenging due to their lower resolution and weaker stereo geometry relative to stereo mode satellites such as WorldView. The quality can be improved by utilizing their much larger numbers of repeat images, but this requires effective stereopair selection. To determine the stereo geometries required for obtaining quality DSMs from PlanetScope Dove imagery, we apply a new methodology for generating simulated stereo images of varying geometries using adjusted orientation parameters obtained by a self-calibrating bundle adjustment and validated by comparing the resulting rigorous sensor and rational function models. The accuracies of simulated stereo and multi-pair DSMs are then assessed through comparison to a reference DSM, providing the relationship between specific imaging geometries and DSM quality. Our results provide a basis for automated stereo imagery selection to enable large-scale DSM production from PlanetScope Dove imagery. Our methodology can be applied to other sources of stereo imagery and designing future satellite missions. In the future, we will further develop multi-pair matching algorithms for generating DSMs with Dove Classic images to improve both accuracy and quality that are otherwise limited by the weak stereo geometry of single stereo pairs.

A CRITERION OF RADIAL DISTORTION ON STRUCTURE FROM MOTION WITH CONSUMER-GRADE CAMERAS

Abstract. Images from consumer-grade cameras typically contain significant radial distortions, resulting in less accurate photogrammetry results generated using Structure from Motion (SfM). It is prone to reconstruct inaccurate scene points, becoming the so-called doming effect. The main reason is that in bundle adjustment, the last step of SfM, radial distortion is insufficiently estimated even though self-calibrating bundle adjustment was carried out. This paper designed a criterion to measure the radial distortion before SfM calculation, called Criterion on Radial Distortion (CRD). Firstly, the feature points were extracted and matched on the image; then, the image was divided into circular blocks, and the fundamental matrices were estimated by the matching points in different ring sub-blocks; finally, the difference between these fundamental matrices was used to reflect the radial distortion. Experiments on simulation data show that CRD can accurately reflect the radial distortion, which verifies its validity. Using actual UAV image data to conduct SfM experiments, CRD is consistent with the self-calibration results of Agisoft Metashape. After evaluating the accuracy of the elevation of scene points, it verifies its necessity for SfM.

Bridge Structural Deformation Monitoring Using Digital Camera

Burgeoning off-the-selves Digital Single Lens Reflector (DSLR) cameras have been gaining attentions as a fast and affordable tool for conducting deformation monitoring of man-made engineering structures. When a sub millimetre of accuracy is sought, deliberate concerns of their usage must be considered since lingering systematic errors in the imaging process plaque such non metric cameras. This paper discusses a close range photogrammetric method to conduct structure deformation monitoring of the bridge using the digital DSLR camera. The bridge is located in Malang Municipality, East Java province, Indonesia. There are more than 100 images of the bridge’s concrete pillars were photographed using convergent photogrammetric network at distance variations between 5m to 30m long on each epoch. Then, the coordinates of around 550 captured retro-reflective markers attached on the pillars facade are calculated using self-calibrating bundle adjustment method. The coordinate differences of the markers from the two consecutive epochs are detected with a magnitude between 0.03 mm to 6 mm with a sub-millimetre precision measurement level. However, by using global congruency testing and a localization of deformation testing, it is confirmed that the bridge pillar’s structures are remain stable between those epochs.

ADAPTIVE WEIGHTING OF IMAGE OBSERVATIONS FOR SELF-CALIBRATION WITH FISHEYE IMAGES

Abstract. Fisheye cameras have been widely used in photogrammetric applications, but conventional techniques must be adapted to consider specific features of fisheye images, such as nonuniform resolution in the images. This work presents experimental results of an adaptive weighting of the observation in a self-calibrating bundle adjustment to cope with the nonuniform resolution of fisheye images. GoPro Fusion and Ricoh Theta dual-fisheye systems were calibrated with bundle adjustment based on equisolid-angle projection model combined with Conrady-Brown distortion model. The image observations were weighted as a function of radial distance based on combining loss of resolution and blurring in fisheye images. The results were compared with a similar trial by considering the same standard deviation for all image observations. The use of adaptive weighting of image observations reduced the estimated standard deviation of unit weight by 30 % and 50 % with GoPro Fusion and Ricoh Theta images, respectively. The estimation of relative orientation parameters (ROPs) was also improved (∼50 %) when using adaptive weighting for image observations.

Validation of preliminary geometric positioning accuracy for China’s civil high-resolution surveying and mapping satellites: Ziyuan-3 01 and Gaofen-7 panchromatic imagery

ABSTRACT The positioning accuracy of Ziyuan-3 01 (ZY-3 01) and Gaofen-7 (GF-7) panchromatic imagery, obtained during the early deployment of the satellites, was verified. Particularly, this paper first validated the geometric positioning accuracy of GF-7 panchromatic imagery. Based on the rigorous geometric model of surveying and mapping satellites, this study applied a geometric calibration (GC) model using the Rodrigues rotation matrix. Also, we employed the self-calibrating bundle adjustment with an additional parameter model to compensate for the internal orientation distortion. The results show that the positioning accuracy can be largely improved with few ground control points. Especially, the horizontal accuracy (specified in x and y) and elevation accuracy of ZY-3 01 in the Anping area are around 4 m and 2 m, respectively, and that of GF-7 in the Beijing area can achieve 3.2 m, 2.8 m, and 1.2 m in the x, y, and z directions, respectively.

AN ITERATIVE APPROACH FOR SELF-CALIBRATING BUNDLE ADJUSTMENT

Abstract. Obtaining accurate image interior and exterior orientations is the key to improve 3D measurement accuracy besides reliable and accurate image matching. A majority of cameras used for those tasks are non-metric cameras. Non-metric cameras commonly suffer various distortions. Generally, there are two ways to remove these distortions: 1) conducting prior camera calibration in a controlled environment; 2) applying self-calibrating bundle adjustment in the application environment. Both approaches have their advantages and disadvantages but one thing is common that there is no universal calibration model available so far which can remove all sorts of distortions on images and systemic errors of image orientations. Instead of developing additional calibration models for camera calibration and self-calibrating adjustment, this paper presents a novel approach which applies self-calibrating bundle adjustment in an iterative fashion: after performing a conventional self-calibrating bundle adjustment, the image coordinates of tie points are re-calculated using the newly obtained self-calibration model coefficients, and the self-calibrating bundle adjustment is applied again in the hope that the remaining distortions and systematic errors will be reduced further within next a few iterations. Using a “virtual image” concept this iterative approach does not require to resample images or/and re-measure tie points during iterations, only costs a few additional iterations computational resource. Several trails under various application environments are conducted using this proposed iterative approach and the results indicate that not only the distortions can be reduced further but also image orientations become much stable after a few iterations.

A GNSS-FREE UNMANNED AERIAL LASER SCANNING SYSTEM

Abstract. In this paper, the procedure of developing and evaluating a UAV-borne mapping system is described. The system is equipped with both a LiDAR and a camera. The system mounting parameters, as well as the intrinsic parameters of the individual sensors, are calibrated rigorously. Simultaneous calibration of the LiDAR intrinsic parameters and the LiDAR-camera mounting parameters is performed in a self-calibrating bundle adjustment with additional relative orientation constraints. A visual-inertial approach is proposed to georeference the laser scans without using a GNSS receiver. This approach is motivated not only by the interest of users in low-cost systems but also by the fact that the integrity of GNSS signals might be affected under several environmental conditions, e.g., indoors, in urban canyons, under tree canopies. It is shown that a low-cost inertial measurement unit not equipped with a dual-frequency, real-time kinematic GNSS receiver is still useful for georeferencing the laser scanning data with cm-level accuracy. The scans are also textured using the images captured by the camera, which enriches the LiDAR point clouds with spectral information.

INVESTIGATION INTO THE BEHAVIOUR AND MODELLING OF CHROMATIC ABERRATIONS IN NON-METRIC DIGITAL CAMERAS

Abstract. Chromatic aberration in colour digital camera imagery can affect the accuracy of photogrammetric reconstruction. Both longitudinal and transverse chromatic aberrations can be effectively modelled by making separate measurements in each of the blue, green and red colour bands and performing a specialized self-calibrating bundle adjustment. This paper presents the results of an investigation with two aims. The first aim is to quantify the presence of chromatic aberration in two sets of cameras: the six individual cameras comprising a Ladybug5 system, calibrated simultaneously in air; and four GoPro Hero 5 cameras calibrated independently under water. The second aim is to investigate the impacts of imposing different constraints in the self-calibration adjustment. To this end, four different adjustment cases were performed for all ten cameras: independent adjustment of the observations from each colour band; combined adjustment of all colour bands’ observations with common object points; combined adjustment of all colour bands with common object points and common exterior orientation parameters for each colour band triplet; and combined adjustment with common object points and certain common interior orientation parameters. The results show that the Ladybug5 cameras exhibit a small (1-2 pixel) amount of transverse chromatic aberration but no longitudinal chromatic aberration. The GoPro Hero 5 cameras exhibit significant (25 pixel) transverse chromatic aberration as well as longitudinal chromatic aberration. The principal distance was essentially independent of the adjustment case for the Ladybug5, but it was not for the GoPro Hero 5. The principal point position and precision were both affected considerably by adjustment case. Radial lens distortion was invariant to the adjustment case. The impact of adjustment case on decentring distortion was minimal in both cases.

Deep Depth from Uncalibrated Small Motion Clip.

We propose a novel approach to infer a high-quality depth map from a set of images with small viewpoint variations. In general, techniques for depth estimation from small motion consist of camera pose estimation and dense reconstruction. In contrast to prior approaches that recover scene geometry and camera motions using pre-calibrated cameras, we introduce in this paper a self-calibrating bundle adjustment method tailored for small motion which enables computation of camera poses without the need for camera calibration. For dense depth reconstruction, we present a convolutional neural network called DPSNet (Deep Plane Sweep Network) whose design is inspired by best practices of traditional geometry-based approaches. Rather than directly estimating depth or optical flow correspondence from image pairs as done in many previous deep learning methods, DPSNet takes a plane sweep approach that involves building a cost volume from deep features using the plane sweep algorithm, regularizing the cost volume, and regressing the depth map from the cost volume. The cost volume is constructed using a differentiable warping process that allows for end-to-end training of the network. Through the effective incorporation of conventional multiview stereo concepts within a deep learning framework, the proposed method achieves state-of-the-art results on a variety of challenging datasets.

Simultaneous All-Parameters Calibration and Assessment of a Stereo Camera Pair Using a Scale Bar

Highly accurate and easy-to-operate calibration (to determine the interior and distortion parameters) and orientation (to determine the exterior parameters) methods for cameras in large volume is a very important topic for expanding the application scope of 3D vision and photogrammetry techniques. This paper proposes a method for simultaneously calibrating, orienting and assessing multi-camera 3D measurement systems in large measurement volume scenarios. The primary idea is building 3D point and length arrays by moving a scale bar in the measurement volume and then conducting a self-calibrating bundle adjustment that involves all the image points and lengths of both cameras. Relative exterior parameters between the camera pair are estimated by the five point relative orientation method. The interior, distortion parameters of each camera and the relative exterior parameters are optimized through bundle adjustment of the network geometry that is strengthened through applying the distance constraints. This method provides both internal precision and external accuracy assessment of the calibration performance. Simulations and real data experiments are designed and conducted to validate the effectivity of the method and analyze its performance under different network geometries. The RMSE of length measurement is less than 0.25 mm and the relative precision is higher than 1/25,000 for a two camera system calibrated by the proposed method in a volume of 12 m × 8 m × 4 m. Compared with the state-of-the-art point array self-calibrating bundle adjustment method, the proposed method is easier to operate and can significantly reduce systematic errors caused by wrong scaling.

A STUDY ON THE VARIATIONS OF INNER ORIENTATION PARAMETERS OF A HYPERSPECTRAL FRAME CAMERA

Abstract. New low-cost hyperspectral frame sensors have created a new perspective for remote sensing applications. In this work, we investigate some issues related to the geometric calibration of a hyperspectral frame camera based of FPI (Fabry-Pérot Interferometer), the Rikola camera. The approach proposed in paper is to study the changes in internal optical path caused by the FPI and by the splitting prism. The aim is to model the changes in the IOPs with an analytical function and also to estimate the misalignments between sensors. Several experiments were performed. The changes in position of a specific point were analasyzed to confirm that the bundle of rays is deviated. A self-calibrating bundle adjustment was performed and the Interior Orientation Parameters (IOP) of each band were estimated. The IOPs were analysed and it was concluded that a single set of symmetrical radial distortion parameters can be used for all band. Also, the estimated parameters for each image band were analysed as a function of the air gap of the FPI interferometer. It was noticed some correlation between the focal length and the air gap, and an air-gap dependent model was estimated. Thus, instead of considering an IOP set for each band or for each sensor, a single set of distortion parameters and another set of parameters that is “air-gap dependent”, was assessed. Another important issue was the determination of the misalignment angles between the two sensors, which can explain some differences in the recovered camera trajectory when performing the bundle adjustment.

ACCURACY ASSESSMENT OF A COMPLEX BUILDING 3D MODEL RECONSTRUCTED FROM IMAGES ACQUIRED WITH A LOW-COST UAS

Abstract. Nowadays, Unmanned Aerial Systems (UASs) are a wide used technique for acquisition in order to create buildings 3D models, providing the acquisition of a high number of images at very high resolution or video sequences, in a very short time. Since low-cost UASs are preferred, the accuracy of a building 3D model created using this platforms must be evaluated. To achieve results, the dean's office building from the Faculty of “Hydrotechnical Engineering, Geodesy and Environmental Engineering” of Iasi, Romania, has been chosen, which is a complex shape building with the roof formed of two hyperbolic paraboloids. Seven points were placed on the ground around the building, three of them being used as GCPs, while the remaining four as Check points (CPs) for accuracy assessment. Additionally, the coordinates of 10 natural CPs representing the building characteristic points were measured with a Leica TCR 405 total station. The building 3D model was created as a point cloud which was automatically generated based on digital images acquired with the low-cost UASs, using the image matching algorithm and different software like 3DF Zephyr, Visual SfM, PhotoModeler Scanner and Drone2Map for ArcGIS. Except for the PhotoModeler Scanner software, the interior and exterior orientation parameters were determined simultaneously by solving a self-calibrating bundle adjustment. Based on the UAS point clouds, automatically generated by using the above mentioned software and GNSS data respectively, the parameters of the east side hyperbolic paraboloid were calculated using the least squares method and a statistical blunder detection. Then, in order to assess the accuracy of the building 3D model, several comparisons were made for the facades and the roof with reference data, considered with minimum errors: TLS mesh for the facades and GNSS mesh for the roof. Finally, the front facade of the building was created in 3D based on its characteristic points using the PhotoModeler Scanner software, resulting a CAD (Computer Aided Design) model. The results showed the high potential of using low-cost UASs for building 3D model creation and if the building 3D model is created based on its characteristic points the accuracy is significantly improved.

A SIMULATION TOOL ASSISTING THE DESIGN OF A CLOSE RANGE PHOTOGRAMMETRY SYSTEM FOR THE SARDINIA RADIO TELESCOPE

Abstract. The Sardinia Radio Telescope (SRT) is a 64 m diameter antenna, whose primary mirror is equipped with an active surface capable to correct its deformations by means of a thick network of actuators. Close range photogrammetry (CRP) was used to measure the self-load deformations of the SRT primary reflector from its optimal shape, which are requested to be minimized for the radio telescope to operate at full efficiency. In the attempt to achieve such performance, we conceived a near real-time CRP system which requires the cameras to be installed in fixed positions and at the same time to avoid any interference with the antenna operativeness. The design of such system is not a trivial task, and to assist our decision we therefore developed a simulation pipeline to realistically reproduce and evaluate photogrammetric surveys of large structures. The described simulation environment consists of (i) a detailed description of the SRT model, included the measurement points and the camera parameters, (ii) a tool capable of generating realistic images accordingly to the above model, and (iii) a self-calibrating bundle adjustment to evaluate the performance in terms of RMSE of the camera configurations.

A SIMULATION TOOL ASSISTING THE DESIGN OF A CLOSE RANGE PHOTOGRAMMETRY SYSTEM FOR THE SARDINIA RADIO TELESCOPE

The Sardinia Radio Telescope (SRT) is a 64 m diameter antenna, whose primary mirror is equipped with an active surface capable to correct its deformations by means of a thick network of actuators. Close range photogrammetry (CRP) was used to measure the self-load deformations of the SRT primary reflector from its optimal shape, which are requested to be minimized for the radio telescope to operate at full efficiency. In the attempt to achieve such performance, we conceived a near real-time CRP system which requires the cameras to be installed in fixed positions and at the same time to avoid any interference with the antenna operativeness. The design of such system is not a trivial task, and to assist our decision we therefore developed a simulation pipeline to realistically reproduce and evaluate photogrammetric surveys of large structures. The described simulation environment consists of (i) a detailed description of the SRT model, included the measurement points and the camera parameters, (ii) a tool capable of generating realistic images accordingly to the above model, and (iii) a self-calibrating bundle adjustment to evaluate the performance in terms of RMSE of the camera configurations.

The Impact of the Calibration Method on the Accuracy of Point Clouds Derived Using Unmanned Aerial Vehicle Multi-View Stereopsis

In unmanned aerial vehicle (UAV) photogrammetric surveys, the cameracan be pre-calibrated or can be calibrated "on-the-job" using structure-from-motion anda self-calibrating bundle adjustment. This study investigates the impact on mapping accuracyof UAV photogrammetric survey blocks, the bundle adjustment and the 3D reconstructionprocess under a range of typical operating scenarios for centimetre-scale natural landformmapping (in this case, a coastal cliff). We demonstrate the sensitivity of the process tocalibration procedures and the need for careful accuracy assessment. For this investigation, vertical (nadir or near-nadir) and oblique photography were collected with 80%–90%overlap and with accurately-surveyed (σ ≤ 2 mm) and densely-distributed ground control.This allowed various scenarios to be tested and the impact on mapping accuracy to beassessed. This paper presents the results of that investigation and provides guidelines thatwill assist with operational decisions regarding camera calibration and ground control forUAV photogrammetry. The results indicate that the use of either a robust pre-calibration ora robust self-calibration results in accurate model creation from vertical-only photography,and additional oblique photography may improve the results. The results indicate thatif a dense array of high accuracy ground control points are deployed and the UAVphotography includes both vertical and oblique images, then either a pre-calibration or anon-the-job self-calibration will yield reliable models (pre-calibration RMSEXY = 7.1 mmand on-the-job self-calibration RMSEXY = 3.2 mm). When oblique photography was Remote Sens. 2015, 7 11934 excluded from the on-the-job self-calibration solution, the accuracy of the model deteriorated(by 3.3 mm horizontally and 4.7 mm vertically). When the accuracy of the ground controlwas then degraded to replicate typical operational practice (σ = 22 mm), the accuracyof the model further deteriorated (e.g., on-the-job self-calibration RMSEXY went from3.2–7.0 mm). Additionally, when the density of the ground control was reduced, the modelaccuracy also further deteriorated (e.g., on-the-job self-calibration RMSEXY went from7.0–7.3 mm). However, our results do indicate that loss of accuracy due to sparse groundcontrol can be mitigated by including oblique imagery.

Rigorous geometric self-calibrating bundle adjustment for a dual fluoroscopic imaging system.

High-speed dual fluoroscopy is a noninvasive imaging technology for three-dimensional skeletal kinematics analysis that finds numerous biomechanical applications. Accurate reconstruction of bone translations and rotations from dual-fluoroscopic data requires accurate calibration of the imaging geometry and the many imaging distortions that corrupt the data. Direct linear transformation methods are commonly applied for performing calibration using a two-step process that suffers from a number of potential shortcomings including that each X-ray source and corresponding camera must be calibrated separately. Consequently, the true imaging set-up and the constraints it presents are not incorporated during calibration. A method to overcome such drawbacks is the single-step self-calibrating bundle adjustment method. This procedure, based on the collinearity principle augmented with imaging distortion models and geometric constraints, has been developed and is reported herein. Its efficacy is shown with a carefully controlled experiment comprising 300 image pairs with 48 507 image points. Application of all geometric constraints and a 31 parameter distortion model resulted in up to 91% improvement in terms of precision (model fit) and up to 71% improvement in terms of 3-D point reconstruction accuracy (0.3-0.4 mm). The accuracy of distance reconstruction was improved from 0.3±2.0 mm to 0.2 ±1.1 mm and angle reconstruction accuracy was improved from -0.03±0.55(°) to 0.01±0.06(°). Such positioning accuracy will allow for the accurate quantification of in vivo arthrokinematics crucial for skeletal biomechanics investigations.

In-flight photogrammetric camera calibration and validation via complementary lidar

This research assumes lidar as a reference dataset against which in-flight camera system calibration and validation can be performed. The methodology utilises a robust least squares surface matching algorithm to align a dense network of photogrammetric points to the lidar reference surface, allowing for the automatic extraction of so-called lidar control points (LCPs). Adjustment of the photogrammetric data is then repeated using the extracted LCPs in a self-calibrating bundle adjustment with additional parameters. This methodology was tested using two different photogrammetric datasets, a Microsoft UltraCamX large format camera and an Applanix DSS322 medium format camera. Systematic sensitivity testing explored the influence of the number and weighting of LCPs. For both camera blocks it was found that when the number of control points increase, the accuracy improves regardless of point weighting. The calibration results were compared with those obtained using ground control points, with good agreement found between the two.

AUTOMATIC CAMERA CALIBRATION FOR CULTURAL HERITAGE APPLICATIONS USING UNSTRUCTURED PLANAR OBJECTS

Abstract. As a rule, image-based documentation of cultural heritage relies today on ordinary digital cameras and commercial software. As such projects often involve researchers not familiar with photogrammetry, the question of camera calibration is important. Freely available open-source user-friendly software for automatic camera calibration, often based on simple 2D chess-board patterns, are an answer to the demand for simplicity and automation. However, such tools cannot respond to all requirements met in cultural heritage conservation regarding possible imaging distances and focal lengths. Here we investigate the practical possibility of camera calibration from unknown planar objects, i.e. any planar surface with adequate texture; we have focused on the example of urban walls covered with graffiti. Images are connected pair-wise with inter-image homographies, which are estimated automatically through a RANSAC-based approach after extracting and matching interest points with the SIFT operator. All valid points are identified on all images on which they appear. Provided that the image set includes a "fronto-parallel" view, inter-image homographies with this image are regarded as emulations of image-to-world homographies and allow computing initial estimates for the interior and exterior orientation elements. Following this initialization step, the estimates are introduced into a final self-calibrating bundle adjustment. Measures are taken to discard unsuitable images and verify object planarity. Results from practical experimentation indicate that this method may produce satisfactory results. The authors intend to incorporate the described approach into their freely available user-friendly software tool, which relies on chess-boards, to assist non-experts in their projects with image-based approaches.