Awesome
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.
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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.
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Each model object has 36 easy views and 36 hard views.(3DR2N2 has 24 easy views)
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Each view of each model object, we have albedo, depth, normal and RGB images.(3DR2N2 has only RGB images)
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different from 3DR2N2, our resolution is 224 * 224 instead of 137 * 137
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All the object is absolutely inside the field of view.
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Camera is looking at origin, but in hard folder, we randomly shift the center of the model at (x_rand,y_rand,z_rand)
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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 |
|---|---|---|---|
| <img src="samples/albedo_1176dff7f0ec879719d740e0f6a9a113/hard/32.png" width="200px" /> | <img src="samples/image_1176dff7f0ec879719d740e0f6a9a113/hard/32.png" width="200px"/> | <img src="samples/depth_1176dff7f0ec879719d740e0f6a9a113/hard/32.png" width="200px"/> | <img src="samples/normal_1176dff7f0ec879719d740e0f6a9a113/hard/32.png" width="200px" /> |
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:
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