readme.md

This directory contains code that generates partial point clouds objects.

To start with:

1. Download and Install Blender

2. Create a list of normalized 3D objects to be rendered, which should be in .obj format, we provide ModelNet_Flist.txt. as a template. We also provide PC_Normalisation.py for normalization.

3. To generate the rendered depth image from 3d objects (you might need to install a few more supportive packages, i.e. Imath, OpenEXR, due to the differences in the development environments)

   # blender -b -P Depth_Renderer.py [data directory] [file list] [output directory] [num scans per model]
   
   blender -b -P render_depth.py ../data/modelnet40 ModelNet_Flist.txt ./dump 10

   The generated intermediate files are in OpenEXR format (*.exr). You can also modify the intrinsics of the camera model in Depth_Renderer.py, which will be automatically saved in the intrinsics.txt.

4. To re-project the partial occluded point cloud from the depth image:

   python EXR_Process.py \
   	--list_file ModelNet_Flist.txt \
       --intrinsics intrinsics.txt \
       --output_dir ./dump \
       --num_scans 10 ;
   

   This will convert the *.exr files into depth images (*.png) then point clouds (*.pcd)

5. Now use OcCo_Torch/utils/LMDB_Writer.py to convert all the pcd files into lmdb dataloader:

   python LMDB_Writer.py \
   	--list_path ../render/ModelNet_Flist.txt \
       --complete_dir ../data/modelnet40 \
       --partial_dir ../render/dump/pcd \
       --num_scans 10 \
       --output_file ../data/MyTrain.lmdb ;

6. Now you can pre-train the models via OcCo on your own constructed data, enjoy :)