cDc edited this page Aug 27, 2016 · 6 revisions

Next an usage example of the available modules is presented. For this we used the Sceaux Castle images and OpenMVG pipeline to recover camera positions and the sparse point-cloud. Please note that all output presented here is the original output obtained automatically by the OpenMVS pipeline, with no manual manipulation of the results. The complete example (including Windows x64 binary for the modules) can be found at OpenMVS_sample.

All OpenMVS binaries support some command line parameters, which are explained in detail if executed with no parameters or with -h.

@FlachyJoe contributed with a script which automates the process of running OpenMVG and OpenMVS in a single command line. Same results as bellow can be obtained by running:

python <images_folder> <output_folder>

Convert scene from OpenMVG

After reconstructing the scene, OpenMVG will generate by default the sfm_data.bin file containing camera poses and the sparse point-cloud. Using the exporter tool, we convert it to the OpenMVS project scene.mvs:

openMVG_main_openMVG2openMVS -i sfm_data.bin -o scene.mvs

To import a scene reconstructed by OpenMVG using the old .json ASCII format run the following:

openMVG_main_openMVG2openMVS -i scene.json -o scene.mvs

A typical sparse point-cloud obtained by the previous steps will look like this:

sparse point-cloud

Dense Point-Cloud Reconstruction (optional)

If there are missing scene parts, the dense reconstruction module can recover them by estimating a dense point-cloud:

DensifyPointCloud scene.mvs

The obtained dense point-cloud (please note the vertex colors are roughly estimated only for visualization, they do not contribute farther down the pipeline):

dense point-cloud

Rough Mesh Reconstruction

The sparse or dense point-cloud obtained in the previous steps is used as the input of the mesh reconstruction module:

ReconstructMesh scene_dense.mvs

The obtained mesh:

rough mesh

Mesh Refinement (optional)

The mesh obtained either from the sparse or dense point-cloud can be further refined to recover all fine details or even bigger missing parts. Next the rough mesh obtained only from the sparse point-cloud is refined:

RefineMesh scene_mesh.mvs

The mesh before and after refinement:

rough mesh refined mesh

Mesh Texturing

The mesh obtained in the previous steps is used as the input of the mesh texturing module:

TextureMesh scene_dense_mesh.mvs

The obtained mesh plus texture:

textured mesh

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