.scene files and world2grid.txt about 3d-sis

[Sekunde]

check out this https://github.com/Sekunde/3D-SIS/tree/master/datagen
you need to run our C++ code to generate .scene file and world2grid.txt file.

[santhu937]

@Sekunde Can u please suggest the proper steps to train the model on my own custom dataset which contains RGB images, depth,.ply files,pose, label

[Sekunde]

You need to generate voxels data (signed distance field) from Depth Images and your annotations (example see https://github.com/Sekunde/3D-SIS/blob/master/datagen/ScanReal/src); you can also generate voxel data from triangle mesh (.ply) (see https://github.com/christopherbatty/SDFGen).

General Introduction to our data generation, see https://github.com/Sekunde/3D-SIS/tree/master/datagen. For RGB images, you can train a 2d semantic network, e.g. ENET.

Basically, our code takes in the data from data loader that has format presented in https://github.com/Sekunde/3D-SIS/blob/master/lib/datasets/dataset.py
(or you can write your own data loader, the output of dataset.py should be identical to ours, then you can just reuse the rest of our code)

[santhu937]

Ok Thank you .

…

On Fri, Mar 13, 2020, 2:55 PM Ji Hou ***@***.***> wrote: You need to generate voxels data (signed distance field) from Depth Images and your annotations (example see https://github.com/Sekunde/3D-SIS/blob/master/datagen/ScanReal/src); you can also generate voxel data from triangle mesh (.ply) (see https://github.com/christopherbatty/SDFGen). General Introduction to our data generation, see https://github.com/Sekunde/3D-SIS/tree/master/datagen. For RGB images, you can train a 2d semantic network, e.g. ENET. Basically, our code takes in the data from data loader that has format presented in https://github.com/Sekunde/3D-SIS/blob/master/lib/datasets/dataset.py (or you can write your own data loader, the output of dataset.py should be identical to ours, then you can just reuse the rest of our code) — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#8 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AF3TKVFPCEWKFPZXP7DB5LLRHH3XRANCNFSM4IGPZVUQ> .