# Bridging Open-Source Photogrammetry: Toward Synergies Between Meshroom, MicMac and others

Anthony Pamart, Ewelina Rupnik, Fabien Castan, Antoine Laurent, Marco Gaiani

► To cite this version:

Anthony Pamart, Ewelina Rupnik, Fabien Castan, Antoine Laurent, Marco Gaiani. Bridging Open-Source Photogrammetry: Toward Synergies Between Meshroom, MicMac and others. 11th International ISPRS / CIPA Workshop "3D Virtual Reconstruction and Visualization of Complex Architectures", Roberto Pierdicca; Ramona Quattrini; Francesco Fassi; Fabio Remondino, Feb 2026, Ancone, Italy. pp.343-350, 10.5194/isprs-archives-XLVIII-2-W12-2026-343-2026. halshs-05512271

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# Bridging Open-Source Photogrammetry : Toward Synergies Between Meshroom, MicMac and others

Anthony Pamart<fnref n="1" />, Ewelina Rupnik<fnref n="2" />, Fabien Castan<fnref n="3" />, Antoine Laurent<fnref n="4" />, Marco Gaiani<fnref n="5" />

**Keywords:** Image-based modeling, range- based modeling, photogrammetry, software, open-source

## Abstract

This paper introduces MicMacRoom, a prototype that bridges the qualitative photogrammetric workflow of MicMac with the intuitive, node-based interface of Meshroom, addressing critical limitations of open-source photogrammetry in usability, interoperability, and sustainability. Developed by a working group of the French National 3D Consortium, MicMacRoom targets Cultural Heritage applications by providing customizable workflows for key processes ; dense cloud generation, meshing, and orthophoto production. As an open-source project, MicMacRoom provides public access to its source code, documentation, and processing templates, laying the foundation for a unified and sustainable photogrammetry ecosystem. The current version of the prototype is a starting point for future developments, focusing on expanding interoperability with additional image-based modeling tools and methods. This article also presents the ongoing integration of colour calibration as a preliminary step for ensuring future compatibility with advanced multi-view photometric workflows.

## 1. Introduction

Photogrammetry spans diverse applications, from cultural heritage preservation to industrial inspection, relying on varied acquisition devices. This diversity underscores the need for adaptable, customizable workflows. Free Libre Open-Source Software (FLOSS) addresses this demand by providing flexible, transparent, and cost-effective solutions. This paper examines FLOSS's pivotal role in photogrammetry and introduces MicMacRoom, a prototype integrating Meshroom and MicMac to improve accessibility and precision in 3D image-based modeling.

Despite the potential of open-source tools in terms of robustness and adaptability, persistent challenges remain in user experience, interoperability, and long-term sustainability. Key obstacles include: (1) a steep learning curve due to insufficient documentation and unintuitive interfaces; (2) fragmented workflows, lacking seamless tool and output integration; and (3) limited interoperability, restricting advanced reconstruction pipelines and hybrid approaches (e.g., RTI, NeRF). This work thus focuses on developing the MicMacRoom prototype, a collaborative solution designed to bridge these gaps, enhance usability, and streamline open-source image-based modeling workflows.

Initiated in 2020 by the Image-Based Modeling and Software Synergies Working Group of the French National 3D Consortium for Humanities and Social Sciences, this project began with an analysis of existing FLOSS solutions, user experience (UX), and feature requirements derived from daily practice. These insights shaped the future interface of MicMac Version 2, tailored for cultural heritage (CH) applications. Ultimately, the collab-

<figure><img src="image_2.png" /><figcaption>Figure 1. Diagram of Image Based modeling paradigm.</figcaption></figure>

oration within our network fostered a strategic alliance between the two leading open-photogrammetry contributors: MicMac, renowned for its rigorous metrological capabilities but hindered by complexity, and Meshroom, praised for its modular, node-based architecture yet lacking certain CH-specific tools.

## 2. State of the art

The scientific positioning of this research-driven software prototype is addressed at two levels: first, a general contextualization in image-based modeling, with a focus on open photogrammetry (Section 2.1); second, an in-depth technical analysis of each component involved in the software prototype.

<footnote n="1">¹ MAP, Modèles et Simulations pour l'Architecture et le Patrimoine, UPR 2002 CNRS, 13009 Marseille</footnote>

<footnote n="2">² LASTIC, IGN/ENSG, Univ. Gustave Eiffel, Paris</footnote>

<footnote n="3">³ Technicolor, Paris, France</footnote>

<footnote n="4">⁴ IRIT, Institut de recherche en informatique de Toulouse, UMR 5505, 31071 Toulouse, France</footnote>

<footnote n="5">⁵ Alma Mater Sudio, University of Bologna</footnote>
## 2.1 Past initiatives and open challenges in open photogrammetry

A critical review of open photogrammetry over the past decade reveals a persistent gap in stable and sustainable solutions. Photogrammetry, positioned at the intersection of geomatics, computer vision, and image processing (Hartley and Mundy, 1993, W. Schindler, 2018), has advanced significantly toward accurate and accessible 3D modeling—often referred to as photogrammetric Structure from Motion (SfM) (Aicardi et al., 2018). Open-source photogrammetry tools have emerged as viable alternatives to commercial software, serving diverse applications such as Cultural Heritage (CH), CGI/VFX, and forensics. However, CH applications demand higher accuracy, advanced quality control, and georeferencing, driving interest in photogrammetric methods. Meanwhile, challenges like massive digitization (complex sites or large datasets) and multimodal approaches (multisensor and incremental processing) are pushing CH users toward SfM methods, which offer improved speed and robustness for unstructured image sets.

As illustrated in Figure 2, FLOSS photogrammetry tools provide as many alternatives as commercial packages (Luhmann, 2016, Schönberger and Frahm, 2016, Schönberger et al., 2016, Lindenberger et al., 2021, Pamart et al., 2020, Sweeney et al., 2015, Griwodz et al., 2021, Morelli et al., 2024). Notably, recent initiatives emphasize cross-library integration (Gonzalez-Aguilera et al., 2018, Ruiz de Oña et al., 2023, Pan et al., 2024). Despite these advancements, FLOSS adoption remains limited, particularly among non-technical CH users, due to insufficient documentation, tutorials, and hands-on training (Verriez et al., 2023). While FLOSS tools are cost-free, they incur hidden costs:

* Performance gaps: Open solutions lag behind commercial alternatives in processing speed and computational efficiency.

* Learning curve: FLOSS tools are less intuitive, with uneven documentation, requiring significant effort for regular use.

* User experience (UX): Many tools lack graphical user interfaces (GUIs) or feature unfriendly UX designs.

Finally, the absence of open standards for photogrammetry project files hinders interoperability for exchange, archiving and reproductibility purposes. While inputs (images, GCPs) and outputs (point clouds, meshes, textures) use exchangeable formats, intermediate data (tie points, depth maps) remain difficult to convert. Some formats, such as Bundler (.out), N-View Matches (.nvm), and COLMAP's data structure, enjoy partial support, with Kaptur emerging as a popular open SfM format and conversion tool. Alternatives like the Open Photogrammetry Format (OPF) and LIGHTCAM (Nocerino et al., 2020) exist but face adoption challenges or discontinuation.

## 2.2 Fostering synergies between open-source image-based modeling

### MicMac [IGN]

MicMac is a free and open-source photogrammetry suite developed since 2003. It generates high-resolution 3D surface models, orthomosaics, and displacement fields from overlapping images. Originally designed for the French National Mapping Agency (IGN), MicMac prioritizes metrological precision and georeferencing accuracy. It has been widely adopted for applications such as cultural heritage documentation (Abergel et al., 2023), dyke surveillance (Tournadre et al., 2015), tectonic plate analysis (Rosu et al., 2015), and glacier volume quantification (Ghuffar et al., 2022). MicMac is integrated into ESA's Geohazard Thematic Platform and FORM@TER for ground deformation monitoring. Since 2021, MicMac v2 (MMVII) has been under development. Since the early development of MicMac, GUI attempts have faced maintenance challenges, with the most reliable interface being v-commands—QT-based Low-Level Interfaces (LLI) automatically generated for new functionalities.

### Alice Vision / Meshroom [Mikros Image - Technicolor]

Meshroom is a node-based visual programming tool designed to create, manage, and automate workflows. It integrates AliceVision nodes and pipelines for computer vision and machine learning tasks. Its nodal architecture allows users to build custom pipelines by connecting nodes, where each node represents a processing step. Meshroom's cache-based invalidation mechanism ensures that only affected downstream nodes are recomputed when parameters change. Nodes can be executed locally or distributed across render farms, enabling parallel processing (Griwodz et al., 2021). Since its 2014 release, Meshroom has been widely adopted in VFX, medical augmented reality (Collins et al., 2021), cultural heritage (Sá et al., 2019, Bici et al., 2020), archaeology (Milàn et al., 2020, Falkingham et al., 2020, Lallensack et al., 2022), biology (Carlucci et al., 2020, Ortega-Jiménez and Sanford, 2021), insect 3D reconstruction (Chowdhury et al., 2021, Plum and Labonte, 2021), video surveillance (Wallner et al., 2021), 3D printing (Horvath and Cameron, 2020, Bellis et al., 2021, Ravi et al., 2021), tourism (Nomikou and et Al., 2020, Poux et al., 2020), virtual reality (Poux et al., 2020, El Saer et al., 2020a, Oersen et al., 2020, El Saer et al., 2020b). Its applications also include forensic analysis (Galanakis et al., 2021, Tóth et al., 2021, Larsen et al., 2021) and building inspections (Yokota and Frangopol, 2021, Merkle et al., 2020, Perry et al., 2020), demonstrating the versatility and adaptability of the solution.

### SHAFT [UNIBO]

(SAT & HUE Adaptive Fine Tuning (SHAFT) (Gaiani et al., 2018) is the target-based Color Calibration (CC) solution developed from 2017 to achieve an accurate apparent color reproduction of the digitized artefacts. SHAFT is fully automated (requires only the target selection) and uses a Calibrite ColorChecker Classic or Passport. The workflow is organized in three steps i) RAW image linearization ii) Exposure compensation and white balance iii) CC through a linear matrix then a per-channel polynomial fitting algorithm (Gaiani et al.,
<table><caption>Figure 2. Overview of 3D image-based modeling software and libraries (Last update 9 January 2026).
In blue, the solutions existing in 2014, in red negative aspects, in green positive ones, in orange mitigated elements, in pink the synergic tools, in violet missing or odd data</caption><thead><tr><th>Solution</th><th>Type</th><th>Process</th><th>Licence</th><th>Provider</th><th>Platform</th><th>GUI</th><th>CLI / API</th><th>Pro</th><th>Cons</th><th>Last release and update frequency</th></tr></thead><tbody><tr><td>Photomodeler</td><td>Software</td><td>Photogrammetry</td><td>Commercial</td><td>Eos System Inc (Canada)</td><td>Windows</td><td>YES</td><td>YES</td><td>Still here</td><td>Not used in CH</td><td>2026.0.0 Dec 2025<br>Quarterly</td></tr><tr><td>Metashape</td><td>Software</td><td>Photogrammetry<br>CV</td><td>Commercial</td><td>Agisoft (Russia)</td><td>Multi</td><td>YES</td><td>YES</td><td>Robust</td><td>Under embargo for some<br>public institutions</td><td>2.3.0 Oct 2025<br>Quarterly</td></tr><tr><td> MicMac (v1)<br>MMVII (v2)</td><td>Library</td><td>Photogrammetry</td><td>FLOSS</td><td>IGN (FR)</td><td>Multi</td><td>YES<br>but NO</td><td>YES</td><td>Completeness<br>Refactored</td><td>Lack of user-friendliness<br>In development</td><td>1.1.1 March 2023<br>Beta First release</td></tr><tr><td>Open-MVG</td><td>Library</td><td>Computer Vision</td><td>FLOSS</td><td>P. Moulon (FR/USA)</td><td>Multi</td><td>NO</td><td>YES</td><td>Maintenance</td><td>No interface</td><td>V.2.1 dec 2023<br>yearly</td></tr><tr><td>Reality Capture</td><td>Software</td><td>Computer Vision</td><td>Commercial</td><td>EPIC Games (USA)</td><td>Multi</td><td>YES</td><td>YES</td><td>Free</td><td>Licencing unpredictable<br>Target community (CGI-<br>VFX)</td><td>V.2.1 Nov 2025<br>yearly</td></tr><tr><td> iTwin Capture<br>Modeler</td><td>Software</td><td>Photogrammetry</td><td>Commercial</td><td>Bentley (USA)</td><td>Windows</td><td>YES</td><td>YES</td><td>Accurate and<br>versatile</td><td>Pricing</td><td>23.0.0.1317 June<br>2025 Yearly</td></tr><tr><td>PIX4DMatic</td><td>Software</td><td>Photogrammetry<br>CV</td><td>Commercial</td><td>PIX4D<br>(Switzerland)</td><td>Windows<br>MacOS</td><td>YES</td><td>YES</td><td>UAV expertise</td><td>Licences</td><td>2.0.0 , jan 2026</td></tr><tr><td>COLMAP</td><td>Library</td><td>Computer Vision</td><td>FLOSS</td><td>J. Schönberger<br>(Switzerland/USA)</td><td>No MacOS</td><td>NO<br>YES</td><td>YES</td><td>Very robust<br>Basemap for NERF</td><td>High GPU/memory<br>demand</td><td>3.11 nov 2024<br>Monthly</td></tr><tr><td> Regard3D</td><td>Software</td><td>Computer Vision</td><td>FLOSS</td><td>R. Hiestand<br>(Switzerland)</td><td>Multi</td><td>YES</td><td>NO</td><td>Cross-library<br>(O-MVG + COLMAP)</td><td>Homebrew</td><td>1.0.0 March 2019<br>End-of-Life</td></tr><tr><td> Theia</td><td>Library</td><td>Computer Vision</td><td>FLOSS</td><td>C. Sweeney UC<br>Santa Barbara<br>(USA)</td><td>No</td><td>NO</td><td>YES</td><td>Velocity</td><td>Infrequent updates</td><td>0.8 Oct 2018<br>End-of-Life</td></tr><tr><td> AliceVision<br>Meshroom</td><td>Library<br>Software</td><td>Computer Vision</td><td>FLOSS</td><td>IRIT / Mikros<br>Technicolor (FR)</td><td>No MacOS</td><td>YES</td><td>YES</td><td>Powerful<br>Expendable</td><td>Computer Vision oriented<br>No GCP, Ortho etc</td><td>2025.1.0 aug 2025<br>Yearly</td></tr><tr><td> Graphos</td><td>Software</td><td>Photogrammetry</td><td>FLOSS</td><td>TIDAL/ISOP (Spain)<br>- ISPRS</td><td>Windows</td><td>YES</td><td>YES</td><td>Cross-library<br>COLMAP + others</td><td>Still in beta</td><td>2.0.0 beta 9 jul 2025<br>Semiannual</td></tr><tr><td> GLOMAP</td><td>Library</td><td>Computer Vision</td><td>FLOSS</td><td>ETH Zurich<br>(Switzerland)</td><td>Multi</td><td>NO</td><td>YES</td><td>Cross-library<br>(COLMAP + Theia +<br>PoseLib)</td><td>Sensitive to suboptimal<br>inputs</td><td>1.2.0 Oct 2025<br>Semiannual</td></tr><tr><td> PxFm</td><td>Library</td><td>Computer Vision</td><td>FLOSS</td><td>ETH Zurich<br>(Switzerland)</td><td>Linux only</td><td>NO</td><td>YES</td><td>Optimization of<br>COLMAP</td><td>Single release</td><td>1.0 may 2023<br>Yearly</td></tr><tr><td> TACO</td><td>Library</td><td>Photogrammetry</td><td>FLOSS</td><td>A.Pamart (CNRS -<br>FR)</td><td>Multi</td><td>NO</td><td>YES</td><td>Optimization /<br>automation of<br>MicMac</td><td>No release yet</td><td>Beta Jan 2022<br>End-of-Life</td></tr><tr><td> PhotoMatch</td><td>Library</td><td>Computer Vision</td><td>FLOSS</td><td>ISPRS (EU)</td><td>No</td><td>NO</td><td>YES</td><td>Multi-libraries<br>features craft-based<br>extraction</td><td>2D Matching only</td><td>2.0.0 Alpha Nov<br>2022<br>End-of-Life</td></tr><tr><td> Deep Image<br>Matching</td><td>Library</td><td>Computer Vision</td><td>FLOSS</td><td>FBK (3D-OM - Italy)</td><td>Multi</td><td>In Dev</td><td>YES</td><td>Multi-libraries<br>features Al-based<br>extraction</td><td>Bridge to 3D SfM</td><td>2.0.0 August 2025<br>Yearly</td></tr></tbody></table>

2017) before a global refinement at patch level to ensure the minimization of Delta E (Mokrzycki and Tatol, 2011). Under controlled lighting, SHAFT yields result with color discrepancies within the 0,5 – 1,5 CIELAB unit range, providing a strong basis for colorimetric reproduction of CH artefacts. SHAFT has already been successfully applied in the field of 3D acquisition through the imaging of ancient paintings (Fantini et al., 2023) and drawings (Apollonio et al., 2015) with stereo photometric and photogrammetric techniques. It has also been applied in the field of historical architecture and larger scale (Remondino et al., 2016), also museum objects using wide range of cameras, from medium format, SLR or mirrorless cameras but also smartphone imagers (Apollo-nio et al., 2025). SHAFT2 will be released soon and will explore the Meshroom's framework to rely on stable and evolutive GUI moreover bridging cross-solution image-based techniques.

This critical review of the state of the art in FLOSS for image-based modeling reveals some remaining challenges:

* Maintenance and sustainability issues: FLOSS initiatives struggle with long-term support.

* Interoperability: Tools often lack complete functionality, forcing users into fragmented workflows without stable exchange formats.

* User accessibility: Many tools require command-line expertise, limiting adoption by non-technical users.

The MicMacRoom prototype addresses these challenges by leveraging the strengths of two leading open-source solutions: Meshroom (with its intuitive GUI and nodal architecture) and MicMac (a powerful yet complex CLI-based tool), while also supporting potential plug-in integrations such as SHAFT2 for color calibration. This paper pursues a twofold objective: (i) presenting the current state of the prototype, which enables the construction and execution of MicMac pipelines within the Meshroom environment, and (ii) outlining our vision for a fully interoperable framework in open image-based modeling.

### 3. MicMacRoom prototype

Development of the MicMacRoom prototype began as a collaborative effort between developers and expert users, which explains the initial inertia in both development and adoption. Key achievements to date include the creation of 27 functional nodes, covering essential photogrammetric processes such as tie-point generation, bundle block adjustment, dense cloud reconstruction, meshing, and texturing (see Figure 4). Additionally, a Node Generator (NG) was developed to seamlessly integrate MicMac commands into Meshroom's modular framework. The project also aims to provide enhanced documentation and tutorials to foster community engagement—both among users and developers—thereby addressing a long-standing barrier to adoption.

The current prototype leverages two native features from each software's source code: the Node Generator (NG) on the Meshroom side and the Low-Level Interface (LLI)
on the MicMac side. The NG inherits from Mesh-
room's core *desc* class, enabling the generation of mod-
ules within the GUI. The LLI, also known as MicMac's
vCommand mode, generates Qt-based graphical modules
from command-line documentation. The prototype's ar-
chitecture, incorporating these components, is illustrated
in Figure 3. Our development relies on Python, en-
suring versatility and ease of implementation. Pipeline
and template management use Meshroom's internal .MG
format—a JSON-derived structure that is both intuitive
and efficient.

The initial prototype was developed during a hackathon
in July 2023 at a partner research unit, bringing together
developers and expert users from both Meshroom and
MicMac. This version underwent alpha-testing with ex-
perienced MicMac users, despite a limited end-user doc-
umentation. In the first half of 2024, beta-testing and tu-
torial creation were assigned to undergraduate students
from the French National School of Geomatic Sciences
(ENSG). This second phase resulted in a forked ver-
sion with expanded features but reduced stability (sev-
eral bugs persist) compared to the hackathon release. In
2026, a new development phase will commence, suppor-
ted by a recently funded project (see Sections 3.4 and 4
for details).

<figure><img src="image_3.png" /><figcaption>Figure 3. Current architecture of the MicMacRoom prototype.</figcaption></figure>

**3.1 Results and discussions**

The current version of MicMacRoom is fully functional
but remains experimental, serving primarily as a research
prototype. To ensure a progressive development strategy,
a roadmap has been established for the next phase of
prototyping, implementation, and dissemination. This
roadmap defines three levels of synergy:

* Low level: Focuses on debugging and optimization to achieve a stable beta version.

* Medium level: Addresses interoperability challenges to enhance workflows and prepare an official release.

* High level: Aims to develop a global framework for broader integration of image-based solutions and scalable deployment.

## 3.2 Open system and single entry point

The preliminary results obtained with the MicMacRoom
prototype demonstrate significant potential for stream-
lining photogrammetric workflows in both terrestrial and
aerial contexts. The system currently supports end-
to-end processing pipelines, enabling the generation of
dense point clouds, textured meshes, and orthophotom-
osaics, while integrating global orientation and georefer-
encing capabilities. These pipelines benefit from Mesh-
room's modular architecture, which allows users to ad-
apt parameters and customize visual outputs, such as
color schemes, labels, and annotations, to meet spe-
cific project requirements. To facilitate accessibility,
the node-based workflows are made available through
two complementary distribution formats. The first con-
sists of preconfigured processing templates—including
MicMacBasic, MicMacOrtho, MicMacTexturedMesh,
and MicMacAeroGCP—which can be directly accessed
via the interface and require only the user’s image data-
set as input. The second format comprises tutorial-based
templates, designed to guide users through reproducible
workflows for various acquisition scenarios, including in-
cremental processing.

The complete resource package, which includes technical
documentation, step-by-step tutorials, sample datasets,
and the full source code, is centralized and accessible in
the repository: https://linkstack.fr/@MicMacRoom.

## 3.3 Limitations

Several limitations persist and must be explicitly commu-
nicated to potential users, particularly regarding known
bugs and missing features. While the ready-to-use
pipelines are customizable to facilitate adoption by non-
expert MicMac users, the original, non-intuitive com-
mand names have been preserved for node labeling. A
revised and simplified naming convention is currently
under consideration (e.g., replacing Tapioca, Tapas,
AperiCloud, and C3DC with 2D Matching, Camera Cal-
ibration and Pose Estimation, Sparse Cloud, and 3D
Dense Matching, respectively). Nevertheless, signific-
ant efforts have been dedicated to clarifying parameter
options and relocating expert features into a advanced
settings tab, thereby simplifying the interface for non-
expert users (see Figure 5). The prototype currently re-
quires a custom installation process, involving the config-
uration of system environment variables to manage de-
pendencies. While this approach may introduce com-
patibility challenges—particularly when integrating ad-
ditional packages (e.g. Python, SHAFT2) with their
own dependencies—the MeshroomResearch environment
was designed to mitigate such issues. The prototype
is compatible only with Windows and Linux platforms
(no MacOS version), though it inherits the hardware de-
pendencies of AliceVision/Meshroom. Beyond installa-
tion constraints, certain core functionalities remain un-
available. Unlike node parametrization, interactions with
processing data—such as point picking, mask generation,
and point cloud visualization—are not yet fully integ-
rated and are prioritized for future development. Finally,
bridging the gap between AliceVision/Meshroom’s incre-
mental, node-based workflow management and MicMac’s
<figure><img src="image_4.png" /><figcaption>Figure 4. Current states of MicMac based the modules and options implemented.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Figure 5. Example of MicMac module (Tapioca) within MeshRoom.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Figure 6. Experimental node of SHAFT2 to manage Color Calibration prior to MicMac-based photogrammetric processing within the Meshroom powered prototype.</figcaption></figure>

strictly procedural (I/O-based) approach remains a critical challenge to achieving seamless interoperability for synergistic workflows.

## 3.4 Future works

The future development strategy adopts a three-tiered approach to progressively enhance synergies between MicMac, AliceVision, and other image-based FLOSS solutions. On the MicMac side, the prototype opens new dissemination avenues for MMVII (MicMac V2), which—unlike its predecessor—is truly multi-platform. The introduction of Python bindings will further expand interaction capabilities with MMVII’s source code, while its modular dense image matching architecture enables the integration of state-of-the-art algorithms into existing pipelines. This modularity, central to the MeshroomResearch framework, lays the groundwork for a versatile open toolbox in image-based modeling. To this end, experimental nodes have already been developed to demonstrate feasibility, including integrations with PointConverter and Python-based scripting (e.g. parsing, data conversion, and management). Given that any command-line interface (CLI) tool can potentially be adapted to Meshroom’s plug-in environment, the long-term goal is to establish a comprehensive open tool-

box for photogrammetric workflows. In parallel, ongoing efforts focus on integrating color calibration via SHAFT2 (previously introduced) into a unified pipeline (see Figure 6). Color calibration (CC) represents a critical preprocessing stage for both photogrammetric/SfM and multi-light image collections (e.g., RTI, PS), particularly for combined approaches (Brument et al., 2024, Laurent et al., 2025). As these advanced multi-view photometric techniques are currently being implemented within the same Meshroom environment—and under the same funding program for open-source software consolidation—their accessibility within a unified, interoperable framework would not only add significant value but also demonstrate the transformative potential of FLOSS synergies.

## 4. Conclusion and perspectives

In this article, we presented MicMacRoom as a proof-of-concept prototype, introducing an open framework designed to enhance image-based modeling. The current beta version enables users to execute complete MicMac-based workflows directly within the Meshroom GUI, as detailed in Section 3. These workflows encompass the full photogrammetric pipeline, including 2D matching, camera calibration and pose estimation, sparse cloud generation, 2D/3D filtering, global orientation and compensation (via GCP and georeferencing), 3D dense matching, meshing, texturing, orthorectification, and mosaicing.
The prototype is fully open-source, providing not only the source code but also comprehensive technical documentation, with a particular emphasis on tutorials and sample datasets to facilitate adoption. Our objective is to build a community of developers and users to support and sustain this initiative, ensuring its long-term evolution. Currently, our prototype relies on three core dependencies: MicMac for the 3D reconstruction pipeline, Meshroom for the GUI environment, and SHAFT2 for color calibration preprocessing, demonstrating the potential for broader synergies among open-source tools. This approach is grounded in a deep understanding of the challenges and needs of open photogrammetry and cultural heritage (CH) applications, as discussed in Section 2.1. While the current version remains experimental, it already addresses fundamental requirements for CH photogrammetry and is extensible to other domains, as explored in Section 2.2. This extensibility underscores its potential for broader impact across diverse applications, even more considering the evolution of multi-view stereo photometric methods.

The work presented in this article, along with the proposed roadmap for future development, serves as the foundation for a recently funded project: COCORICO (Consolidation of Open-source and Collaborative Software for Resilient Interoperable Photogrammetric Communities). This initiative embodies our vision of a sustainable, community-driven ecosystem in open photogrammetry, exemplified by the MicMacRoom prototype. The project has been selected under the OPEN program (CNRS Innovation), which specifically targets the technological development and transfer of open-source solutions. The primary objective of COCORICO is to deliver a stable, official release of the MicMacRoom prototype, prior to expanding its scope toward open-core systems and SaaS deployment, as illustrated in Figure 7. Additionally, the project will integrate complementary prototypes to enhance its functionality, including semantic annotation with AIOLI (Abergel et al., 2023), virtual tour generation with MYRTE (Bergerot, 2024), metadata management with ANAMNESIS (Pamart et al., 2026), on-site SLAM-based capture with DURAIR (De Luca et al., 2026).

Ultimately, this research work aspires to establish a unified open photogrammetry framework, integrating and enhancing existing and emerging FLOSS image-based solutions. By optimizing workflows and expanding functional capabilities, this initiative seeks to redefine the open image-based modeling paradigm, rendering it more accessible, efficient, and adaptable—not only for Cultural Heritage applications but also for diverse domains beyond.

## Acknowledgements

The current version of the prototype has been funded by the National 3D Consortium for Human and Social Science through subcontracting and financial support for the hackathon and internships. A special thanks to Gregoire De Lillo, lead developer of the initial version of the prototype. The authors would also like to thank their colleagues J.M. Muller, F. Comte and D. Jouin for their participation in the hackathon (see Taxonomy

<figure><img src="image_7.png" /><figcaption>Figure 7. Diagram of MicMacRoom envisioned synergies.</figcaption></figure>

of Credit in section below). The team as a whole would like to thank all the alpha testers.

## Roles and contributions

Contributors: Anthony Pamart (AP), Ewelina Rupnik (ER), Fabien Castan (FCA), Antoine Laurent (AL), Marco Gaiani (MG), Gregoire de Lillo (GDL), Benoit Maujean (BM), Florent Comte (FCO), Jean-Michael Muller (JMM), Andrea Ballabeni (AB). CRediT (https://credit.niso.org/): Conceptualization: AP; Data curation: AP; Formal analysis: AP; Funding acquisition: AP; Investigation: AP; Methodology: AP; Project administration: AP; Resources: AP, FCA, AL; Software: AP, ER, FCA, MG, GDL, BM, FCO, JMM, AB; Supervision: AP; Validation: AP; Visualization: AP; Writing—original draft: AP, ER, FCA, MG; Writing—review & editing: AP, ER, FCA, MG.

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