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Science ResearchTop 9 Best 3D Photo Scanning Software of 2026
Compare 3D Photo Scanning Software for 3D models, with a factual ranking that covers Pix4Dmapper, COLMAP, and COLMAP alternatives.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Pix4Dmapper
Project-based georeferencing with camera parameters and optional ground control points tied to CRS.
Built for fits when teams need repeatable geospatial processing and controlled automation without algorithm-level API exposure..
COLMAP
Editor pickBundle adjustment with a reconstruction model exported as cameras, poses, and 3D points.
Built for fits when teams need offline photogrammetry automation and access to reconstruction artifacts..
OpenMVG
Editor pickIncremental and global SfM reconstruction with bundle adjustment using match graph and track data.
Built for fits when teams need reproducible, scriptable sparse reconstruction artifacts for a larger pipeline..
Related reading
Comparison Table
The comparison table ranks Agisoft Metashape, Pix4Dmapper, and COLMAP and maps their integration depth, data model choices, and automation surfaces to concrete workflows. It also compares admin and governance controls such as RBAC, audit log coverage, and provisioning options, alongside API surface and extensibility for batch throughput and custom pipelines. Readers can use the matrix to evaluate schema fit, configuration effort, and how each tool’s automation patterns affect repeatability across datasets.
Pix4Dmapper
mapping photogrammetryProcesses geotagged and overlapping imagery into orthomosaics, DSMs, and 3D models with scalable photogrammetry pipelines.
Project-based georeferencing with camera parameters and optional ground control points tied to CRS.
Pix4Dmapper’s core workflow is image processing into dense outputs like point clouds and textured meshes, followed by orthomosaic and measurement-ready exports. The tool models spatial inputs through cameras, tie points, and optional ground control points linked to a coordinate reference system, which supports consistent georeferencing across runs. Processing can be configured for different throughput needs by reusing project settings and controlling what outputs are generated per job.
A key tradeoff is that deep customization of the processing pipeline depends on how the job is configured rather than exposing every internal algorithm stage to an external API. It fits best when an organization needs controlled production of geospatial deliverables from repeatable capture patterns, not when teams require full algorithm-level extensibility. A common usage situation is recurring inspections or surveys where the same project schema and coordinate setup must produce consistent orthos and point clouds.
- +Georeferencing pipeline maps camera and GCP inputs to consistent CRS outputs
- +Repeatable processing projects support predictable orthomosaic and point cloud production
- +Dense output generation covers mesh, point cloud, and orthomosaic deliverables
- +Job-based automation supports scripted runs with controlled configurations
- +Project assets integrate with capture workflows for end-to-end survey processing
- –External API access is limited to job and configuration controls, not internal algorithm stages
- –High customization workflows require project-level configuration rather than code hooks
- –Complex governance depends on how projects and roles are set up outside mapper itself
- –Automation visibility can require careful log and artifact management per processing job
Best for: Fits when teams need repeatable geospatial processing and controlled automation without algorithm-level API exposure.
More related reading
COLMAP
open-source SFM/MVSPerforms structure-from-motion and dense multi-view stereo reconstruction from images using an academic-grade, open-source pipeline.
Bundle adjustment with a reconstruction model exported as cameras, poses, and 3D points.
COLMAP generates a reconstruction schema that includes cameras, image poses, and 3D points, which can be exported for downstream tooling. The workflow is driven by explicit pipeline stages such as feature extraction, feature matching, sparse reconstruction, and optional dense reconstruction. Integration depth is strongest at the file and command level, because the outputs map directly to common photogrammetry representations.
A key tradeoff is that there is limited admin and governance surface, so multi-user provisioning, RBAC, and audit logs are not part of the core product. This fits teams that run batch jobs on shared compute and manage access controls at the filesystem and job-scheduling layer. A common usage situation is offline reconstruction at scale, where throughput and deterministic command parameters matter more than interactive management.
- +Command-line automation supports repeatable batch reconstruction
- +Explicit data model exports cameras, poses, and 3D points
- +Configurable pipeline stages enable controlled processing runs
- +Dense reconstruction supports downstream mesh and point-cloud workflows
- –No built-in RBAC or audit logs for shared environments
- –Admin and governance controls rely on external infrastructure
- –Tuning requires photogrammetry expertise to avoid failures
Best for: Fits when teams need offline photogrammetry automation and access to reconstruction artifacts.
OpenMVG
open-source SfMReconstructs camera poses and sparse 3D structure from images through open-source SfM algorithms.
Incremental and global SfM reconstruction with bundle adjustment using match graph and track data.
Integration depth is driven by reproducible CLI commands and well-defined inputs and outputs for each reconstruction stage. The pipeline consumes images plus camera and feature parameters, then writes intermediate and final artifacts like match graphs, tracks, camera poses, and sparse point clouds. Extensibility comes from feeding customized configurations into each stage and exchanging model artifacts with other photogrammetry components. Automation and API surface are practical through shell scripting and wrapper code that manages invocation order and artifact paths.
A key tradeoff is that OpenMVG expects operators to handle orchestration, storage layout, and format compatibility across stages. The tool also focuses on reconstruction stages rather than end-to-end project management, so governance and admin controls depend on external job schedulers and filesystem permissions. Fits well when a team needs batch throughput, strict reproducibility, and schema-compatible model outputs for a larger pipeline. A common usage situation is producing sparse reconstructions for scan bootstrapping before densification with separate software.
- +CLI pipeline covers feature extraction, matching, and global reconstruction steps
- +Exports camera poses, intrinsics, and sparse structure for downstream stages
- +Configuration-driven runs support repeatable batch throughput
- +File-based intermediate artifacts enable pipeline orchestration and reruns
- –No built-in RBAC, audit log, or job governance controls
- –Operator must manage orchestration, formats, and artifact storage
- –Limited interactive project management compared with UI-first scanners
Best for: Fits when teams need reproducible, scriptable sparse reconstruction artifacts for a larger pipeline.
More related reading
OpenMVS
open-source MVSConverts sparse reconstructions into dense point clouds, meshes, and textured models using open-source multi-view stereo methods.
Stage-based reconstruction pipeline that outputs reusable depth maps and camera-aligned geometry artifacts.
OpenMVS is a command-line photogrammetry and multi-view stereo workflow built around offline processing of camera images into depth, meshes, and textured geometry. Integration is mostly file- and pipeline-driven, so organizations typically connect it via scripts that manage inputs, process steps, and outputs rather than via an application API.
The data model is expressed through intermediate artifacts such as camera parameters, depth maps, and reconstruction outputs that can be cached and re-run for repeatability. Extensibility and automation rely on adding preprocessing, orchestration, and postprocessing around the OpenMVS executables, with governance handled outside the tool through job scheduling, RBAC, and audit logging in the surrounding system.
- +Deterministic CLI pipeline using intermediate reconstruction artifacts and caching
- +Clear separation of stages like depth estimation, meshing, and texturing
- +Works well in batch throughput workflows managed by external schedulers
- +Configurable processing parameters passed via command-line options
- –No built-in API surface for direct programmatic job submission and status
- –Automation usually requires custom scripting and pipeline orchestration
- –Admin and RBAC controls are external, with no native audit log support
- –Integration depth depends on filesystem conventions and artifact management
Best for: Fits when teams need reproducible, batch 3D reconstruction pipelines with external orchestration.
Meshroom
node-based photogrammetryBuilds photo-based 3D reconstructions by running node-based SfM and MVS pipelines using AliceVision components.
Meshroom node graph based AliceVision pipeline with stage-level configuration and CLI execution.
Meshroom turns photo sets into 3D reconstructions using the AliceVision photogrammetry pipeline. It runs as a node graph workflow with defined inputs, intermediate artifacts, and final dense outputs.
The project emphasizes reproducible configuration via graph settings and CLI execution, which supports automation beyond interactive GUI use. Integration depth is limited to file-based workflows, so orchestration typically needs external scripting around exported results.
- +Node graph workflow captures inputs, parameters, and intermediate reconstruction artifacts
- +AliceVision pipeline components support configurable reconstruction stages
- +CLI execution enables batch throughput and repeatable runs
- +Exports dense meshes and textures suitable for downstream 3D pipelines
- –No documented API for job orchestration, RBAC, or audit logging
- –Automation relies on external scripts and file-level handoffs
- –Data model lacks schema controls for multi-user governance
- –Throughput depends on local compute setup and manual resource coordination
Best for: Fits when teams need local, scriptable photogrammetry workflows with file-based integrations.
More related reading
Bonito
AI 3D from imagesReconstructs 3D geometry from image sequences using deep learning-based NeRF-style training workflows.
API-driven scan and job automation that ties reconstruction outputs into a controlled data model.
Bonito fits teams that need 3D photo capture outputs to flow into existing engineering pipelines with repeatable configuration. The data model centers on scans, assets, and processing jobs, which supports consistent handoffs from acquisition through reconstruction.
Bonito provides an API surface for automation and integration, plus extensibility points for custom workflows around capture, processing, and delivery. Admin controls focus on workspace governance, with RBAC and audit visibility used to manage access across projects and users.
- +API supports automation of scan creation, processing runs, and asset retrieval
- +Consistent data model links scans to jobs, outputs, and downstream assets
- +Extensibility supports workflow customization around reconstruction steps
- +Workspace governance supports RBAC for project-level access boundaries
- +Audit log visibility helps track configuration changes and user activity
- –Automation depends on correct schema mapping for downstream storage
- –Throughput can bottleneck on job scheduling when many scans run concurrently
- –Admin setup requires upfront project structure and access design
- –Custom integration logic increases operational overhead for small teams
Best for: Fits when teams need automated 3D scan workflows with API-driven provisioning and controlled access.
Luma AI
cloud NeRFCreates interactive 3D scenes from captured photos and videos with browser-based capture and cloud reconstruction.
API-based scan jobs that generate exportable 3D assets for automated downstream processing.
Luma AI differentiates through a scan-to-asset workflow aimed at producing consistent 3D outputs from photos with an automation and API surface. The core capabilities center on photogrammetry-style reconstruction, asset export, and downstream integration where 3D results feed rendering, previewing, and storage pipelines.
Integration depth is strongest when the organization relies on programmable ingestion, configurable processing, and repeatable asset generation at scale. Governance quality shows up through access control, auditing, and environment controls that support provisioning and operational oversight across teams.
- +API-driven scanning lets pipelines trigger 3D reconstruction from existing ingestion flows.
- +Produces exportable 3D assets that support predictable downstream rendering workloads.
- +Configurable processing parameters enable repeatable results across similar captures.
- +Integrates into automated asset lifecycles with fewer manual steps.
- –Output quality depends heavily on input capture consistency and photo coverage.
- –Schema and metadata mapping require additional work for enterprise data models.
- –Complex multi-stage automation needs careful orchestration to handle retries.
- –Real-time monitoring and throughput controls are limited compared to bespoke pipelines.
Best for: Fits when teams need API automation for recurring 3D photo scanning jobs with governed access.
More related reading
Polycam
mobile 3D captureGenerates textured 3D meshes and point clouds from photos with real-time capture and reconstruction workflows.
Mobile-to-scan reconstruction inside the Polycam capture app
Polycam turns captured scenes into 3D photo scans using mobile capture and in-app reconstruction workflows. Exports support common downstream uses like visualization, measurement, and asset handoff, with project-level organization for repeated captures.
Integration depth is limited compared to enterprise scanning systems since API and automation capabilities are not framed as a first-class governance surface. The data model centers on scan projects and derived assets rather than a documented schema with administration controls.
- +Mobile capture workflow reduces setup friction for on-site scanning
- +Project organization keeps multiple captures and exports grouped
- +Supports downstream handoff through standard 3D output formats
- –API and automation surface is not documented for admin-grade workflows
- –Extensibility lacks a visible schema for governed asset metadata
- –RBAC, audit log, and provisioning controls are not positioned for enterprise governance
Best for: Fits when teams need quick 3D photo scans and export workflows with minimal IT governance.
RealityScan
mobile photogrammetryProduces photogrammetry-based 3D models from mobile photos and camera captures with automatic reconstruction.
Quixel pipeline integration for scan outputs, including mesh and texture asset handoff to Unreal workflows.
RealityScan creates photogrammetry-driven 3D meshes and texture data from captured images. It integrates with Quixel pipelines for asset ingestion, viewing, and downstream use in Unreal Engine workflows.
The data model centers on scan outputs, map assets, and exportable mesh formats, with project structures that align to asset creation stages. Automation and integration depth depend on how Quixel and Unreal tooling expose scan results through APIs, webhooks, or scripted import flows.
- +Image-to-mesh and texture generation tailored for Quixel asset workflows
- +Export formats align with Unreal Engine asset ingestion pipelines
- +Project structure supports repeatable scan-to-asset output organization
- –API automation and extensibility surface is not clearly defined for admin workflows
- –RBAC, provisioning, and audit log controls are not specified in the product surface
- –Data model schema details for scan outputs and variants are not exposed for governance
Best for: Fits when teams need image-to-asset scanning inside Quixel and Unreal content pipelines.
Conclusion
After evaluating 9 science research, Pix4Dmapper stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right 3D Photo Scanning Software
This buyer’s guide covers 3D photo scanning software for producing 3D models, dense point clouds, and textured assets from overlapping images. It focuses on Pix4Dmapper, COLMAP, and the supporting open-source and API-driven options including OpenMVG, OpenMVS, Meshroom, Bonito, Luma AI, Polycam, and RealityScan.
The guide explains how to compare integration depth, the underlying data model and schema, automation and API surface, and admin and governance controls. It also highlights where automation is file-and-command-line driven in COLMAP, OpenMVG, OpenMVS, and Meshroom, versus API and governed workspace models in Bonito, Luma AI, and the Quixel-connected RealityScan.
Image-to-3D reconstruction tools that turn photo sets into governed, usable geometry assets
3D photo scanning software processes overlapping imagery into camera poses and 3D structure using feature extraction, matching, and bundle adjustment, then optionally densifies geometry into meshes, textured models, or orthomosaics. Pix4Dmapper connects camera parameters, optional ground control points, and a repeatable project workflow to produce consistent CRS-tied outputs.
COLMAP and OpenMVG focus on transparent, exportable reconstruction artifacts such as cameras, poses, intrinsics, and 3D points that downstream systems can consume. Teams typically use these tools for survey-grade georeferencing, engineering measurement workflows, digital twin asset creation, and content-pipeline scans that must feed predictable exports and storage processes.
Evaluation criteria mapped to integration, automation, data model, and governance
Integration depth matters because production environments need predictable handoffs between ingestion, processing, and asset delivery. Pix4Dmapper uses project-based processing jobs and consistent CRS outputs, while Bonito and Luma AI expose API-driven scan creation and processing runs tied to a controlled data model.
Automation and API surface matter because batch throughput depends on whether jobs can be provisioned and tracked programmatically. Admin and governance controls matter because multi-user deployments require RBAC, audit log visibility, and audit-friendly processing artifacts rather than ad hoc file drops from OpenMVG, OpenMVS, or Meshroom.
Project-first processing models with CRS-tied outputs
Pix4Dmapper maps camera and optional ground control points to consistent coordinate reference system outputs through project-based georeferencing. This reduces variance across repeatable runs where COLMAP or OpenMVG export reconstructions but do not provide built-in georeferencing governance.
Transparent SfM and reconstruction exports for downstream pipelines
COLMAP exports a reconstruction model with cameras, poses, and 3D points, and it performs bundle adjustment as a core stage. OpenMVG also exports camera intrinsics, poses, and sparse structure using deterministic SfM command-line runs, which supports custom measurement and rendering integrations.
Stage-based densification with cached intermediate artifacts
OpenMVS separates depth estimation, meshing, and texturing into a stage-based CLI pipeline that outputs reusable depth maps and camera-aligned artifacts. OpenMVS and OpenMVS-adjacent workflows reduce reprocessing cost by caching intermediate files, which is less governed in file-only integrations like Meshroom.
Node-graph reconstruction workflows with stage-level configuration
Meshroom runs an AliceVision node graph where graph settings define intermediate artifacts and final dense outputs. This stage-level configuration supports repeatable CLI execution but it does not position an internal API or multi-user schema controls for governance.
API-driven scan provisioning, job automation, and governed access
Bonito provides an API for scan creation, processing runs, and asset retrieval, and it links scans to jobs and outputs within a consistent data model. Luma AI also uses API-based scan jobs to generate exportable 3D assets, while RealityScan integrates into Quixel and Unreal content pipelines where scan outputs and asset handoffs are the integration focus.
Admin-grade RBAC and audit visibility for multi-user operations
Bonito highlights workspace governance with RBAC and audit log visibility to manage access across projects and users. COLMAP, OpenMVG, OpenMVS, and Meshroom rely on external infrastructure for shared-environment governance, with no built-in RBAC or audit logs.
A decision workflow for selecting a 3D scanning tool by integration depth and controls
Start by deciding whether the pipeline must be API-driven or file-and-command-line driven. Bonito and Luma AI expose API-driven scan and job automation, while COLMAP, OpenMVG, OpenMVS, and Meshroom run as scriptable CLI or node-graph workflows with explicit intermediate exports.
Next align the data model to the target asset workflow. Pix4Dmapper emphasizes a project-based georeferencing data model tied to CRS outputs, while COLMAP and OpenMVG emphasize reconstruction structure exports that downstream systems can store and analyze with custom schemas.
Pick the automation surface: API jobs or CLI and node-graph runs
If internal systems must provision scans and trigger processing through programmatic calls, choose Bonito or Luma AI since both expose API-driven scan jobs and processing automation. If the processing system is already built around scripts and batch execution, choose COLMAP, OpenMVG, OpenMVS, or Meshroom since each supports repeatable command-line runs or node-graph CLI execution.
Align the data model to where governance lives
For geospatial workflows that require CRS-consistent outputs, Pix4Dmapper ties camera parameters and optional ground control points to repeatable project outputs. For pipelines that store reconstruction artifacts and build custom downstream logic, COLMAP and OpenMVG export cameras, poses, intrinsics, and sparse 3D structure that can feed internal schemas.
Map stage separation to iteration and throughput needs
For teams that need cached intermediate artifacts to avoid full reprocessing, choose OpenMVS since it outputs reusable depth maps and stage-based geometry artifacts from sparse reconstructions. For node-graph workflows with explicit stage-level configuration, choose Meshroom because it captures inputs and intermediate artifacts in a node graph that can run via CLI.
Verify governance controls for shared environments
If multiple users need RBAC boundaries and audit log visibility for configuration changes, choose Bonito since it includes workspace governance with RBAC and audit visibility. If governance must be implemented outside the tool, choose COLMAP, OpenMVG, OpenMVS, or Meshroom because they lack built-in RBAC and audit log controls and depend on external orchestration.
Confirm which integration gaps exist for algorithm-level customization
If algorithm-level stage customization is required via code hooks, Pix4Dmapper is limited since external API access focuses on job and configuration controls rather than internal algorithm stages. If custom algorithm control is the goal, COLMAP and OpenMVG are better aligned because they expose reconstruction artifacts such as cameras, poses, and sparse structure for downstream control and experimentation.
Which organizations should match specific 3D photo scanning tools to their pipeline shape
Different tools map to different pipeline requirements, especially around API surface, reconstruction artifact access, and governance controls. Pix4Dmapper targets repeatable geospatial processing where CRS outputs must stay consistent across runs. COLMAP and OpenMVG target offline reconstruction pipelines where exports like cameras, poses, and 3D points are the primary integration contract.
The API-driven segment also differs. Bonito and Luma AI focus on API automation for scan creation and asset retrieval with governed access, while Polycam and RealityScan optimize for scan workflows that fit existing content creation pipelines with less admin-grade schema exposure.
Survey and geospatial teams that need repeatable CRS-tied outputs
Pix4Dmapper fits teams that require project-based georeferencing where camera parameters and optional ground control points map into consistent coordinate reference system outputs with predictable orthomosaic and dense deliverables.
Engineering and research pipelines that need reconstruction exports for custom logic
COLMAP and OpenMVG fit teams that want command-line automation plus exportable reconstruction models such as cameras, poses, intrinsics, and sparse 3D structure for downstream measurement, rendering, and custom integration.
Batch reconstruction systems that need stage caching and external orchestration
OpenMVS fits organizations that run batch pipelines and want reusable intermediate artifacts like depth maps and camera-aligned geometry while governance is handled by an external scheduler with RBAC and audit logging.
Enterprise scan workflows that require API provisioning and governed workspace access
Bonito fits teams needing API-driven scan creation, processing job automation, and asset retrieval tied to a controlled data model with RBAC and audit visibility. Luma AI fits similar API automation needs where configurable processing produces exportable 3D assets for downstream lifecycles.
Content pipelines tied to existing Unreal and Quixel asset ingestion
RealityScan fits workflows that depend on Quixel integration where scan outputs and mesh and texture asset handoffs align with Unreal Engine content ingestion rather than exposing a detailed internal schema for admin governance.
Pitfalls that break integration depth, automation, and governance expectations
Common mistakes come from mismatching the pipeline’s automation surface with the tool’s exposed controls. Another frequent failure is assuming built-in governance exists in tools that emphasize local CLI or file-based reconstruction workflows.
The fixes below focus on concrete gaps like missing RBAC, limited API access to algorithm internals, and metadata mapping complexity for enterprise storage and schemas.
Assuming built-in RBAC and audit logs exist in open-source CLI tools
COLMAP, OpenMVG, OpenMVS, and Meshroom provide command-line or file-based pipelines but they lack built-in RBAC and audit logs. Governance must be implemented in the surrounding orchestration layer, including job scheduling permissions and audit trail collection.
Expecting algorithm-stage API hooks from Pix4Dmapper
Pix4Dmapper supports automation through job and configuration controls, but external API access does not expose internal algorithm stages. For algorithm-level experimentation, use reconstruction-export-first tools like COLMAP and OpenMVG that export cameras, poses, intrinsics, and sparse structure.
Overbuilding enterprise schemas without validating schema mapping for API platforms
Bonito and Luma AI expose API automation and governed access, but automation can depend on correct schema mapping for downstream storage. Before broad rollout, validate scan-to-job-to-output mapping so configuration changes and asset records can be tracked without manual remediation.
Choosing a tool without stage caching strategy for high iteration cycles
If the pipeline iterates on meshing or texturing frequently, OpenMVS fits because it outputs reusable depth maps and stage-based geometry artifacts. Meshroom can provide stage-level configuration via its node graph, but organizations still need external orchestration and file handoffs to manage caching behavior.
How We Selected and Ranked These Tools
We evaluated Pix4Dmapper, COLMAP, OpenMVG, OpenMVS, Meshroom, Bonito, Luma AI, Polycam, and RealityScan on features, ease of use, and value because these tools target different pipeline integrations and operational realities. The overall rating reflects a weighted average where features carry the most weight, ease of use and value each contribute one-third, and governance and integration fit are treated as practical feature outcomes rather than marketing claims.
The ranking emphasizes how each tool connects into automation systems through exposed job controls, API-driven provisioning, command-line repeatability, or reconstruction artifact exports. Pix4Dmapper stands apart because project-based georeferencing ties camera parameters and optional ground control points to CRS-consistent outputs, which lifts the features factor for geospatial repeatability while keeping ease of use and value high for controlled production workflows.
Frequently Asked Questions About 3D Photo Scanning Software
Which tool fits repeatable georeferenced drone workflows with controlled outputs?
Which option exposes reconstruction artifacts for custom downstream measurement or rendering pipelines?
How do Pix4Dmapper, COLMAP, and OpenMVG differ in their underlying reconstruction data models?
Which tools support offline batch processing when orchestration is handled by a job scheduler?
What integration approach works best for file-based pipelines versus API-first provisioning?
Which platform is designed for scan job automation and governed access across teams?
How should teams handle data migration from an existing photogrammetry workflow to a new system?
Which toolchain is better suited for dense mesh generation and texture output from photos?
What technical setup issues commonly affect results, and which tools make those constraints visible?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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