Photogrammetry pipeline using OpenMVG and OpenMVS.
(Also includes CMVS and COLMAP).
You can either use spedenaave/dpg image from the docker hub or build it by yourself by first cloning the repository and then using docker build -t dpg . command.
Be adviced that the default setting of docker for windows and mac only give the container access to a very limited amount of system resources. You can increase the amount of cpu cores and memory from the advanced settings.
This method will build and install required binaries directly to your linux installation. It is not recommended!
git clone https://github.com/rennu/dpg /tmp/dpg && cd /tmp/dpg
sudo ./build.sh
In this short example we first clone an example dataset to /tmp/example, then start docker with -v argument to mount /tmp/example to /datasets inside the container, and finally we the run pipeline.
git clone https://github.com/openMVG/ImageDataset_SceauxCastle /tmp/example
docker run -v /tmp/example:/datasets --rm -it spedenaave/dpg
pipeline.py --input /datasets/images --output /datasets/output --sfm-type incremental --geomodel f --run-openmvg --run-openmvs
You should now have a reconstructed model at /tmp/example/omvs folder. Use meshlab or something similar to open it. The end result should look something like this:
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)
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