From the architecture point of view, the unique feature of MicMac with respect to its alternatives is that the user can operate on different software levels, cf. The least experienced user can steer the processing by a simple command line where little or no parameters must be set. An experienced user can operate from the same command line by adapting the parameters typically filled with default values. An expert in photogrammetry can access any parameter at any processing step via a set of XML files (cf.
On top of that, developers and scientists can use MicMac as a library, implement their own algorithms, and share them for the benefit of photogrammetric community. This said, MicMac was designed primarily as a tool for professionals and consequently is far from a model design in the sense of HCI 2.
access to intermediary results in open data formats allowing to interact with the processing chain at any desired stage,. qualitative evaluation of the results via quality indicators (e.g. Bundle block adjustment residuals, covariance matrices, parameter’s sensitivity, correlation images),. a wide range of camera calibration models (e.g. The models adapted to consumer grade cameras, large-frame aerial cameras, cameras with very long focal lengths, fish-eye and spherical cameras),. two-dimensional dense image matching for deformation studies,.
processing of frame camera and pushbroom sensor images,. processing of scanned analogue images,. architecture adapted to big datasets.
Free Photogrammetry Software For Mac Os
Software organisation. MicMac is organised in a number of modules, accessible through a common command mm3d. Execution with a unique command has a twofold advantage: (a) it allows to factorize certain developments while keeping the binary files compact, and from the user point of view, (b) it is easy to remember and gives instantaneous access to all modules (cf. Invoking a selected module is followed by a number of parameters. The obligatory parameters must be specified at first, and since they do not carry names, their order of occurrence is important.
In the example shown in Fig., the obligatory parameter would be the format specification, while the change coordinate file, ChSys, serves as an option. The algorithmic aspects The photogrammetric workflow encompasses the passage from images, through the estimation of the orientation parameters, finalizing with a 3D surface model. In other words, it is a passage from a 2D representation of the world captured by a camera, through inference of the position and rotation of that camera at the moment of the image taking, towards a 3D restitution of the lost dimension.
As long as the quality of the final output depends on the skillful engineering of many small processing steps, estimation of the camera orientation parameters and the matching algorithms constitute the heart of the pipeline. Respectively, the coming sections concentrate on these two aspects, report on the adopted methods and give a global look on what is possible in MicMac.
Structure from motion Recovery of the structure (i.e. 3D coordinates of the observed scene) and the model. It is well known that this model (i.e. The transformation from 3D to 2D space and vice-versa also referred to as collinearity equation) is not linear, hence requires linearization. Moreover, there exists no direct algorithm able to compute orientation parameters globally consistent with a number of images (generally n3). To overcome this gap, bootstrap solutions were proposed.
Using direct algorithms for a single image, a pair or triplet of images, the global orientation is deduced sequentially or hierarchically starting from a seed image pair. The so-obtained parameters serve as input to a system of equations composed of the linearized collinearity equations, where their optimal values – in the stochastic sense – are found iteratively. The observations (e.g.
Tie points) are redundant therefore the solution is deduced with the least squares method by minimizing an objective function. The typical function is the squared differences between the nominal observations and those predicted from the estimated model, possibly subject to constraints.
The latter stage is also known as the bundle block adjustment (BBA). MicMac implementation.
tie points,. lines,. perspective centers derived from direct georeferencing (i.e.
GNSS),. ground control points (GCP),. the lever-arm vector separating the GNSS antenna and the perspective center,.
rigid relative orientation between cameras. On top of that, a broad spectrum of camera calibration models are available. Both argumentation are supported, the physical and the phenomenological.
The former models any imaging error attributed to a phenomenon of a physical nature, while the latter models solely the effects of the lens imperfections, without interrogating its causes. The total distortion is defined as a composition of predefined elementary distortions, such as radial, decentric or a generic polynomial.
Typically, the major distortion are modeled by the physical model, while the generic polynomial models remove the less significant remaining systematism. The critical element of the BBA’s mathematical model is the observation weighting, known as the stochastic model. MicMac specifies three different weighting strategies. The first strategy corresponds to the weighting in the classical model known in photogrammetry – the Gauss-Markov – where the observations are weighted by their true standard deviations known a priori. The second strategy controls the influence of a particular category of observations so as to avoid solutions that are driven by a single category only for the sheer reason of its abundant observations. The last strategy addresses the robustness. The weight is a function of the observation’s residual in the BBA, therefore giving more credibility to observations that are close to the estimated model, and contrary, limiting the influence of observations with high residuals.
User’s viewpoint. There are two principal modules that handle the camera orientation recovery with the simplified interface – Tapas, Campari – and both of the modules call the parent orientation tool Apero (cf. Tapas calculates the purely relative orientation of images, using observed tie points as the only input. Since at this point there is no a priori on positions and rotations of the cameras, Tapas also entails the initialization step where it progressively reconstructs the cameras with the help of the direct orientation algorithms, and intertwines it with the BBA routine. Unlike Tapas, Campari is a BBA module adapted to handle heterogeneous observations.
Besides the tie points, it works with GCPs, the GNNS data and can impose various constraints on the solution. This module is typically executed once a good relative orientation is established, and the cameras are moved two a coordinate system (CS) consistent with that of auxiliary observations, e.g. The latter is performed with any Bascule tool, and it is a spatial similarity transformation (cf. Both Campari and all variations of the Bascule can be regarded as georeferencing tools.
Since recently, MicMac provides the user with the internal accuracy estimates of the BBA parameters (their standard deviations and correlations) derived from covariance matrices. Multi-view stereo image matching (MVSM) Given several overlapping images with known orientation parameters, MVSM is the process of reconstructing a complete 3D model of the scene by finding corresponding pixels (i.e. Their disparities or depths, respectively) between image pairs, and triangulating them in space. The generic algorithm is defined as an energy minimization problem that searches for a disparity map minimizing the energy. The minimization can be solved with local, semi-global and global algorithms. Today the golden standard method for producing dense 3D models from images is the semi-global matching. Image matching breaks down into three processing stages: computation of the pixel matching cost, cost aggregation and disparity calculation.
The matching cost is a measure of dissimilarity, i.e. Describes the unlikelihood that two pixels belong to a unique point in 3D.
As the matching can be ambiguous, and the minimum cost insignificant, an additional a priori is imposed on the energy function that penalizes disparity changes at neighboring pixels (the case of local and semi-global algorithms). This aggregation takes place within a window, a 1D path (locally) or multiple paths (semi-globally). In the latter case, the cost for a given disparity is an accumulated cost along many paths that end in the pixel in question.
MicMac implementation. The inconvenience of the Malt GeomImage is that for a complete 3D restitution of a surface, one must indicate several master and their relevant slave images. As long as for simple object geometries it is feasible, it becomes inefficient for more complex and extensive objects. The fully automated tools overcome this impediment by making this choice for the user, while prioritising completeness and precision of the output.
The two automated tools are C3DC and PIMs which furnish a point cloud and a set of depth maps, respectively. To proceed with the orthophotomap generation, the PIMs2MNT and subsequently the Tawny shall be executed (an exemplar orthophoto in Fig.
The latter collects the per-image orthophotos and merges it to a single mosaic. The individual orthophotos can be created either by PIMs2MNT tool or Malt; cf. 8 Forteresse de Salses, photo acquired from a drone, in collaboration with Map-CNRS/ Left: hypsometry and shading maps overlapped, right: an orthophotography Besides the classical 1D matching (along an image ray, an image row or the Z-coordinate) for reconstruction of object geometry, MicMac also implements a 2D matching strategy. This strategy is useful in 2D deformation studies between rectified images (cf. Fig.; also MM2DPosSism), as an orientation quality estimation tool to asses the remaining y-parallax ( MMTestOrient; cf. ), or in cases when orientation parameters are unknown or known with poor precision (executed with XML file).
Because the expected disparities pertinent to geometry are of higher frequencies than those along the y-parallax, the regularization of the energy function in the two direction is managed separately; cf. Lastly, in the event where all camera optical centers are located on a 3D line, parallel to the surveyed object, and the reconstruction is defined in object space, the matching becomes ambiguous along the epipolar lines. To avoid the ambiguities and the resultant noise, MicMac calculates the matching in ortho-cylindrical coordinates (see RepLocBascule). Interactive tools.
9 Graphical interface to launch commands All results produced by MicMac are exclusively in open data formats (tif, xml, ply) and as such it opens up the possibility to interact with the processing chain at any desirable stage. For instance, the user can import interior and exterior image orientation data and proceed with the dense matching and orthophoto generation. Or vice versa, the orientation parameters or epipolar images can be generated to proceed with an alternative dense image matcher. The reader is advised to refer to the software documentation for more details on the conversion procedures. Image measurement tools In the following, several tools for image measurements and the use of 2D/3D masks are discussed.
The SaisieAppuisInitQT/SaisieAppuisPred icQT tools allow for GCPs’ image measurements (cf. Provided that the camera orientation parameters are known with a reasonable accuracy and expressed in a CS coherent with the GPCs, the latter tool serves as a point position prediction tool. Measurements necessary to perform a spatial similarity transformation, i.e. The origin, the axes directions and distances, can be collected within another georeferencing tool – the SaisieBascQT. These tools normally precede the georeferencing operations with GCPBascule and/or Campari.
11 Interface for measuring GCPs in images Visualization tools To visualize the intermediary and final results MicMac offers a range of different tools. The SEL tool visualizes the tie points between a stereo pair. The AperiCloud tool converts the result of the BBA, that is the poses and 3D tie points, to the point cloud ply format. The Vino tool is a simple image viewer adapted to visualization of very large images (i.e. In size of a few gigabytes), irrespective of their type (i.e. Integer, float or 8Bit).
Moreover, it lets the user to modify the histogram of the image or generate an image crop. Other tools allow a more intuitive visualization of depth maps. For instance, the to8bits tool converts 32-bit or 16-bit images to 8-bit images, while the GrShade tool computes shading from a depth map image (cf. Lastly, the Nuage2Ply transforms a depth map into a point cloud ply format (cf., as the software’s reference webpage;., for technical support;., for the source code. The high priority on-going developments and short-term perspectives are those concerning effective processing of big datasets.
The developments pertain to (a) the tie points extraction, (b) the SfM, as well as (c) the storage of 3D data. Regarding the tie points, very precise tie point detectors and detectors invariant to affine distortions are being developed. Within the SfM, global, structure-less methods – are under development. As a further perspective, adequate methods for storing very large 3D point clouds will be conceived. Lastly, as there has been numerous demands to create a GUI, computer programmers willing to contribute are strongly encouraged to contact the team of developers. A GUI in form of a stand-alone application or a GUI integrated with other open-source software tools, e.g. QGIS, are possible.
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