Create README.md

README.md

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+---
+'[object Object]': null
+language:
+- en
+license: other
+license_name: autodesk-non-commercial-3d-generative-v1.0
+license_link: LICENSE.md
+tags:
+- make-a-shape
+- pc-to-3d
+---
+---
+# Model Card for Make-A-Shape Point Cloud to 3D Model
+
+This model is part of the Make-A-Shape paper, capable of generating high-quality 3D shapes from point clouds with intricate geometric details, realistic structures, and complex topologies.
+
+## Model Details
+
+### Model Description
+
+Make-A-Shape is a novel 3D generative framework trained on an extensive dataset of over 10 million publicly-available 3D shapes. The point cloud to 3D model is one of the conditional generation models in this framework. It can efficiently generate a wide range of high-quality 3D shapes from four view-specific images as inputs. The model uses a wavelet-tree representation and adaptive training strategy to achieve superior performance in terms of geometric detail and structural plausibility.
+
+- **Developed by:** Ka-Hei Hui, Aditya Sanghi, Arianna Rampini, Kamal Rahimi Malekshan, Zhengzhe Liu, Hooman Shayani, Chi-Wing Fu
+- **Model type:** 3D Generative Model
+- **License:** Autodesk Non-Commercial (3D Generative) v1.0
+
+For more information please look at the [Project](https://www.research.autodesk.com/publications/generative-ai-make-a-shape/) [Page](https://edward1997104.github.io/make-a-shape/) and [the ICML paper](https://proceedings.mlr.press/v235/hui24a.html).
+
+### Model Sources
+
+- **Repository:** [https://github.com/AutodeskAILab/Make-a-Shape](https://github.com/AutodeskAILab/Make-a-Shape)
+- **Paper:** [ArXiv:2401.11067](https://arxiv.org/abs/2401.11067),   [ICML - Make-A-Shape: a Ten-Million-scale 3D Shape Model](https://proceedings.mlr.press/v235/hui24a.html)
+- **Demo:** [Google Colab](https://colab.research.google.com/drive/1XIoeanLjXIDdLow6qxY7cAZ6YZpqY40d?usp=sharing)
+
+## Uses 
+
+### Direct Use 
+
+This model is released by Autodesk and intended for academic and research purposes only for the theoretical exploration and demonstration of the Make-a-Shape 3D generative framework.  Please see [here](https://github.com/AutodeskAILab/Make-a-Shape) for inferencing instructions. 
+
+### Out-of-Scope Use 
+
+The model should not be used for:
+
+- Commercial purposes 
+
+- Creation of load-bearing physical objects the failure of which could cause property damage or personal injury 
+
+- Any usage not in compliance with the [link to license], in particular, the "Acceptable Use" section. 
+
+## Bias, Risks, and Limitations 
+
+### Bias 
+
+- The model may inherit biases present in the publicly-available training datasets, which could lead to uneven representation of certain object types or styles. 
+
+- The model's performance may degrade for object categories or styles that are underrepresented in the training data. 
+
+### Risks and Limitations 
+
+- The quality of the generated 3D output may be impacted by the quality and clarity of the input image. 
+
+- The model may occasionally generate implausible shapes, especially when the input image is ambiguous or of low quality.  Even theoretically plausible shapes should not be relied upon for real-world structural soundness.  
+
+## How to Get Started with the Model 
+
+Please refer to the instructions [here](https://github.com/AutodeskAILab/Make-a-Shape).
+
+## Training Details 
+
+### Training Data 
+
+The model was trained on a dataset of over 10 million 3D shapes aggregated from 18 different publicly-available sub-datasets, including ModelNet, ShapeNet, SMPL, Thingi10K, SMAL, COMA, House3D, ABC, Fusion 360, 3D-FUTURE, BuildingNet, DeformingThings4D, FG3D, Toys4K, ABO, Infinigen, Objaverse, and two subsets of ObjaverseXL (Thingiverse and GitHub).
+
+### Training Procedure
+
+#### Preprocessing 
+
+Each 3D shape in the dataset was converted into a truncated signed distance function (TSDF) with a resolution of 256³. The TSDF was then decomposed using a discrete wavelet transform to create the wavelet-tree representation used by the model.
+
+#### Training Hyperparameters
+
+- **Training regime:** Please refer to the paper.
+
+#### Speeds, Sizes, Times 
+
+- The model was trained on 48 × A10G GPUs for about 20 days, amounting to around 23,000 GPU hours.
+- The model can generate shapes within two seconds for most conditions.
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+The model was evaluated on a test set consisting of 2% of the shapes from each sub-dataset in the training data, as well as on the entire Google Scanned Objects (GSO) dataset, which was not part of the training data.
+
+#### Factors
+
+The evaluation considered various factors such as the quality of generated shapes, the ability to capture fine details and complex structures, and the model's performance across different object categories.
+
+#### Metrics
+
+The model was evaluated using the following metrics:
+- Intersection over Union (IoU)
+- Light Field Distance (LFD)
+
+### Results
+
+The point cloud to 3D model achieved the following results on the "Our Val" dataset with 2500 input points:
+- LFD: 1857.84
+- IoU: 0.7595
+
+
+## Technical Specifications 
+
+### Model Architecture and Objective 
+
+The model uses a U-ViT architecture with learnable skip-connections between the convolution and deconvolution blocks. It employs a wavelet-tree representation and a subband adaptive training strategy to effectively capture both coarse and fine details of 3D shapes. 
+
+### Compute Infrastructure
+
+#### Hardware
+
+The model was trained on 48 × A10G GPUs.
+
+## Citation 
+
+**BibTeX:**
+```latex
+@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},
+}
+```