Example call for boulder problem

/opt/dpg/pipeline.py --input /datasets/images/jim.zip --output /datasets/output --sfm-type incremental --geomodel f --run-openmvg --run-openmvs --output-obj

OpenMVG + OpenMVS Pipeline

Photogrammetry pipeline using OpenMVG and OpenMVS.

(Also includes CMVS and COLMAP).

Installation

Windows:

1. Install Windows Subsystem for Linux (Ubuntu 16.04)
2. Clone repository
3. sudo ./build.sh

Linux (Ubuntu 16.04):

1. Clone repository
2. sudo ./build.sh

Docker:

1. Clone repository
2. docker build -t dpg .
3. docker run -v $(pwd):/datasets --rm -it dpg

Examples

Mesh Reconstruction with Textures by using Incremental Structure from Motion

1. Download example image set, open it up in terminal and run the docker image (see above)
2. Run pipeline:

/opt/dpg/pipeline.py --input /datasets/images --output /datasets/output --sfm-type incremental --geomodel f --run-openmvg --run-openmvs

3. Open your model for example using meshlab. The model is named "scene_mesh_refine_texture.ply" and it's under $datasets/ImageDataset_SceauxCastle/sfm/mvs directory

You should end up with something like this (press ctrl/cmd-k to disable backface culling)

Dense Mesh Reconstruction with Textures by using Incremental Structure from Motion

1. Download example image set, open it up in terminal and run the docker image (see above)
2. Run pipeline:

/opt/dpg/pipeline.py --input /datasets/images --output /datasets/output_dense --sfm-type incremental --geomodel f --run-openmvg --run-openmvs --densify

The end result should look something like this

Pipeline Options

General Options:

    --help
        Print this text

    --debug
        Print commands and exit

    --input [directory]
        Image input directory

    --output [directory]
        Output directory

    --sfm-type [string]
        Select SfM mode from Global SfM or Incremental SfM. Possible values:
        incremental
        global

    --run-openmvg
        Run OpenMVG SfM pipeline

    --run-openmvs
        Run OpenMVS MVS pipeline

Optional settings:

    --recompute
        Recompute everything

    --openmvg [path]
        Set OpenMVG install location

    --openmvs [path]
        Set OpenMVS install location

OpenMVG

    Image Listing:

        --cgroup
            Each view have it's own camera intrinsic parameters

        --flength [float]
            If your camera is not listed in the camera sensor database, you can set pixel focal length here.
            The value can be calculated by max(width-pixels, height-pixels) * focal length(mm) / Sensor width

        --cmodel [int]
            Camera model:
            1: Pinhole
            2: Pinhole Radial 1
            3: Pinhole Radial 3 (default)
            4: Pinhole brown
            5: Pinhole with a simple Fish-eye distortion

    Compute Features:

        --descmethod [string]
            Method to describe an image:
                SIFT (default)
                AKAZE_FLOAT
                AKAZE_MLDB

        --dpreset [string]
            Used to control the Image_describer configuration
                NORMAL
                HIGH
                ULTRA

    Compute Matches:

        --ratio [float]
            Nearest Neighbor distance ratio (smaller is more restrictive => Less false positives)
            Default: 0.8

        --geomodel [char]
            Compute Matches geometric model:
            f: Fundamental matrix filtering (default)
                For Incremental SfM
            e: Essential matrix filtering
                For Global SfM
            h: Homography matrix filtering
                For datasets that have same point of projection

        --matching [string]
            Compute Matches Nearest Matching Method:
            BRUTEFORCEL2: BruteForce L2 matching for Scalar based regions descriptor,
            ANNL2: Approximate Nearest Neighbor L2 matching for Scalar based regions descriptor,
            CASCADEHASHINGL2: L2 Cascade Hashing matching,
            FASTCASCADEHASHINGL2: (default)
                * L2 Cascade Hashing with precomputed hashed regions, (faster than CASCADEHASHINGL2 but use more memory).

    Incremental SfM:

        --icmodel [int]
            The camera model type that will be used for views with unknown intrinsic
            1: Pinhole
            2: Pinhole radial 1
            3: Pinhole radial 3 (default)
            4: Pinhole radial 3 + tangential 2
            5: Pinhole fisheye

    Global SfM:

        --grotavg [int]
            1: L1 rotation averaging [Chatterjee]
            2: L2 rotation averaging [Martinec] (default)

        --gtransavg [int]
            1: L1 translation averaging [GlobalACSfM]
            2: L2 translation averaging [Kyle2014]
            3: SoftL1 minimization [GlobalACSfM] (default)


OpenMVS

    --output-obj
        Make OpenMVS output obj instead of ply (default)

    DensifyPointCloud:

        --densify
            Enable dense reconstruction
            Default: Off

        --densify-only
            Only densify (duh)

        --dnumviews [int]
            Number of views used for depth-map estimation
            0 all neighbor views available
            Default: 4

        --dnumviewsfuse [int]
            Minimum number of images that agrees with an estimate during fusion in order to consider it
            inliner
            Default: 3

        --dreslevel [int]
            How many times to scale down the images before point cloud computation. For better accuracy/speed width
            high resolution images use 2 or even 3
            Default: 1

    ReconstructMesh:

        --rcthickness [int]
            ReconstructMesh Thickness Factor
            Default: 2

        --rcdistance [int]
            Minimum distance in pixels between the projection of two 3D points to consider them different while
            triangulating (0 to disable). Use to reduce amount of memory used with a penalty of lost detail
            Default: 2

    RefineMesh:

        --rmiterations [int]
            Number of RefineMesh iterations
            Default: 3

        --rmlevel [int]
            Times to scale down the images before mesh refinement
            Default: 0

        --rmcuda
            Use CUDA version of RefineMesh binary (will fall back the executable is not found)

    Texture Mesh:

        --txemptycolor [int]
            Color of surfaces OpenMVS TextureMesh is unable to texture.
            Default: 0 (black)