Make-A-Shape

This is the official codebase for the ICML paper "Make-A-Shape: a Ten-Million-scale 3D Shape Model"

Project Page, ICML paper, Models, Colab demo

Tasks

• Single-view to 3D inference code
• Multi-view to 3D inference code
• 16³ resolution Voxel to 3D inference code
• 32³ resolution Voxel to 3D inference code
• Google Colab demo
• Point cloud to 3D inference code
• Unconditional 3D generation inference code

Getting Started

Installation

• Python >= 3.10
• Install CUDA if available
• Install PyTorch according to your platform: https://pytorch.org/get-started/locally/
• Install other dependencies by pip install -r requirements.txt

For example, on AWS EC2 instances with PyTorch Deep learning AMI, you can setup the environment as follows:

conda create -n make-a-shape python==3.10
conda activate make-a-shape
pip install -r requirements.txt

Inference

Single-view to 3D

The input data for this method is a single-view image of a 3D object.

python run.py --model_name ADSKAILab/Make-A-Shape-single-view-20m --images examples/single_view/table.png --output_dir examples --output_format obj

Multi-view to 3D

For multi-view input, the model utilizes multiple images of the same object captured from different camera angles. These images should be named according to the index of the camera view parameters as described in Data Formats

python run.py --model_name ADSKAILab/Make-A-Shape-multi-view-20m --multi_view_images examples/multi_view/000.png examples/multi_view/006.png examples/multi_view/010.png examples/multi_view/026.png --output_dir examples --output_format obj

Voxel to 3D (16³ Resolution )

This model uses a voxelized representation of the object with a resolution of 16³. The voxel file is a JSON containing the following keys: resolution, occupancy, and color

python run.py --model_name ADSKAILab/Make-A-Shape-voxel-16res-20m --voxel_files examples/voxel/horse_16.json --output_dir examples --output_format obj

Voxel to 3D (32³ Resolution )

Similar to the 16³ resolution model, but with higher resolution of 32³.

python run.py --model_name ADSKAILab/Make-A-Shape-voxel-32res-20m --voxel_files examples/voxel/horse_32.json --output_dir examples --output_format obj

Point Cloud to 3D

This model uses a point cloud representation of the object.

python run.py --model_name ADSKAILab/Make-A-Shape-point-cloud-20m --pointcloud_files examples/pointcloud/plane.h5df --output_dir examples --output_format obj

Data Formats

• Single-View Input: A single image file (e.g., .png, .jpg) depicting the 3D object.

• Multi-View Input: A set of image files taken from different camera angles. The filenames correspond to specific camera parameters. Below is a table that maps the index of each image to its corresponding camera rotation and elevation:

  Index	Rotation (degrees)	Elevation (degrees)
  0	57.37	13.48
  1	36.86	6.18
  2	11.25	21.62
  3	57.27	25.34
  4	100.07	9.10
  5	116.91	21.32
  6	140.94	12.92
  7	99.88	3.57
  8	5.06	11.38
  9	217.88	6.72
  10	230.76	13.27
  11	180.99	23.99
  12	100.59	-6.37
  13	65.29	-2.70
  14	145.70	6.61
  15	271.98	0.15
  16	284.36	5.84
  17	220.28	0.07
  18	145.86	-1.18
  19	59.08	-13.59
  20	7.35	0.51
  21	7.06	-7.82
  22	146.05	-15.43
  23	182.55	-5.17
  24	341.95	3.29
  25	353.64	9.75
  26	319.81	16.44
  27	233.76	-8.56
  28	334.96	-2.65
  29	207.67	-16.79
  30	79.72	-21.20
  31	169.69	-26.77
  32	237.16	-27.06
  33	231.72	25.91
  34	284.84	23.44
  35	311.22	-14.09
  36	285.15	-7.42
  37	257.11	-14.38
  38	319.14	-23.75
  39	355.62	-9.06
  40	0.00	60.00
  41	40.00	60.00
  42	80.00	60.00
  43	120.00	60.00
  44	160.00	60.00
  45	200.00	60.00
  46	240.00	60.00
  47	280.00	60.00
  48	320.00	60.00
  49	360.00	60.00
  50	0.00	-60.00
  51	90.00	-60.00
  52	180.00	-60.00
  53	270.00	-60.00
  54	360.00	-60.00

• Voxel Input: A JSON file containing a voxelized representation of the object. The JSON includes:

  • resolution: The grid size of the voxel space (e.g., 16 or 32).
  • occupancy: The indices of occupied voxels.
  • color: The RGB values for each occupied voxel.

• Point Cloud Input: A HDF5 file containing a point cloud representation of the object.

Google Colab Demo

To quickly try out the Make-A-Shape model without setting up your local environment, check out the Google Colab Demo.

Validation Dataset

The validation dataset used in the paper is a selection of objects from 19 different open-source 3D shape datasets. The full validation dataset is available for download here.

Citation

BibTeX:

@InProceedings{pmlr-v235-hui24a,
  title = 	 {Make-A-Shape: a Ten-Million-scale 3{D} Shape Model},
  author =       {Hui, Ka-Hei and Sanghi, Aditya and Rampini, Arianna and Rahimi Malekshan, Kamal and Liu, Zhengzhe and Shayani, Hooman and Fu, Chi-Wing},
  booktitle = 	 {Proceedings of the 41st International Conference on Machine Learning},
  pages = 	 {20660--20681},
  year = 	 {2024},
  editor = 	 {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix},
  volume = 	 {235},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {21--27 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://raw.githubusercontent.com/mlresearch/v235/main/assets/hui24a/hui24a.pdf},
  url = 	 {https://proceedings.mlr.press/v235/hui24a.html},
}