Plaster reproduction of Temple of the Dioskouroi from Middlebury Multi-View Stereo Dataset (312 views sampled on a hemisphere) (https://vision.middlebury.edu/mview/data/)
Images captured on a Stanford Spherical Gantry (http://graphics.stanford.edu/projects/gantry/) and camera intrinsic and extrinsic matricies accompany each shot.
We are given Matricies K containing info about the x and y focal length of the camera, along with the principal offset (x and y), R the rotation matrix, and t the translation vector along with our images.
Description of Camera Intrinsic Matricies: http://ksimek.github.io/2013/08/13/intrinsic/
Focal Length given by ,
Principal Point Offset given by ,
Description of Camera Extrinsic Matricies: http://ksimek.github.io/2012/08/22/extrinsic/
Rotational Matrix given by -
Translational Vector given by
Note: We add a fourth row in our extrinsic matrix to make it a 4x4 matrix. The fourth row corresponds to the 4th row of a 4x4 identity matrix.
We use Open3D to create a dense voxel grid using the create_dense function. We set the size of each voxel as follows...
So for us, we used a 2x2x2 cubic voxel dense, the images we were given were 640 x 480. so the size of each voxel was 2/640 = 0.003125.
We also used Open3D to do the carving. To implement the carving we created a silhouette and grabbed the camera parameters for each image, and passed them to the carve_silhouette method.
(Setting Camera Params- http://www.open3d.org/docs/release/python_api/open3d.camera.PinholeCameraParameters.html)
(Creating Voxel Dense and Carving- http://www.open3d.org/docs/0.8.0/python_api/open3d.geometry.VoxelGrid.html)
Finally we use draw_geometries to show the result of voxel carving.
(Displaying- http://www.open3d.org/docs/latest/python_api/open3d.visualization.html)
Note: draw_geometries did not work on Mac with pip install of Open3D, it worked on Debian Linux though.
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