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ShapenetRender_more_variation

A new shapenet rendering 2D image dataset that also contains deph map, normal map and albedo map.

Please cite our paperDISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction (NeurIPS 2019) if you plan to download the rendered images or use our code to render by yourself.

@inProceedings{xu2019disn,
  title={DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction},
  author={Xu, Qiangeng and Wang, Weiyue and Ceylan, Duygu and Mech, Radomir and Neumann, Ulrich},
  booktitle={NeurIPS},
  year={2019}
}

Code contact: Qiangeng Xu* and Weiyue Wang*

Also please cite Shapenet's original paper as well.

Dataset Intro:

The categories included are: cat_ids = { "watercraft": "04530566", "rifle": "04090263", "display": "03211117", "lamp": "03636649", "speaker": "03691459", "cabinet": "02933112", "chair": "03001627", "bench": "02828884", "car": "02958343", "airplane": "02691156", "sofa": "04256520", "table": "04379243", "phone": "04401088" }

Our rendering is based on the convention of 3DR2N2's 2d image rendering.

  • Our script support both shapenet v1 and v2, for v2 you need to change render_blender.py's random range since some model always get out of the field of view. The tar files we provide is rendered on v1.

  • Each model object has 36 easy views and 36 hard views.(3DR2N2 has 24 easy views)

  • Each view of each model object, we have albedo, depth, normal and RGB images.(3DR2N2 has only RGB images)

  • different from 3DR2N2, our resolution is 224 * 224 instead of 137 * 137

  • All the object is absolutely inside the field of view.

  • Camera is looking at origin, but in hard folder, we randomly shift the center of the model at (x_rand,y_rand,z_rand)

  • We set camera Pitch to 0 degree since in most case in real world, the floor is flat.(We assume camera to the origin is along Z axis, the rotation along Z is 0 degree)

albedo RGB Depth normal

In each folder, there is a meta file: rendering_metadata.txt: each line represent a parameter:

camera Yaw camera Roll camera Pitch distance ratio (0 to 1) Focal length in mm Sensor size in mm max real distance x_rand y_rand z_rand
74.77100786318874 37.07793266268725 0 0.6451202137421064 35 32 1.75 -0.1529439091682434 -0.13056571781635284 0.0746786817908287

Dataset download:

image.tar

albedo.tar

depth.tar

normal.tar

Or you can run the generation script by yourself :

  install blender 2.79 and go to its python3.5m to install pip3, then install numpy and opencv
  
  python -u render_batch --model_root_dir {model root dir} --render_root_dir {where you store images} --filelist_dir {which models you want to render} --blender_location {you} --num_thread {10} --shapenetversion {support v1, v2} --debug {False}

Transformation matrix calculation:

Please refer to cam_read.py