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dataset_out=/dataset/openMVS_sample-master
# Initialize the scene (spherical camera model)
openMVG_main_SfMInit_ImageListing -i $dataset -o $dataset_out/matches -f 3.4
# Compute of the features (you can add -n <THREAD_COUNT> to be faster)
openMVG_main_ComputeFeatures -i $dataset_out/matches/sfm_data.json -o $dataset_out/matches -n 2
# Compute the image matches (notice the angular mode, more adapted for spherical images)
openMVG_main_ComputeMatches -i $dataset_out/matches/sfm_data.json -o $dataset_out/matches
# Compute of the camera motion and structure of the scene
openMVG_main_IncrementalSfM -i $dataset_out/matches/sfm_data.json -m $dataset_out/matches -o $dataset_out/reconstruction
#
# MVS (optional)
#
# Convert the scene from OpenMVG to OpenMVS data format
openMVG_main_openMVG2openMVS -i $dataset_out/reconstruction/sfm_data.bin -o $dataset_out/reconstruction/scene.mvs -d $dataset_out/reconstruction/openmvs_images
And here is my DensifyPointCloud-2005241555208B4734.log
I happened to use docker due to some building errors, which means I didn't use Viewer but CCViewer instead. I also tried to run tutorial_demo.py on my dataset, which from my point of view, two datasets share same attributes, such as large overlap between images, however nothing come out under reconstruction_sequential directory, and the result under reconstruction_global is very strange.
Here is my script,
And here is my DensifyPointCloud-2005241555208B4734.log
Could anyone help me to find out why???
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