Sketchmodeling
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Zdog | 8,965 |  | 9 | 20 | 2 years ago | 8 | January 22, 2022 | 44 | JavaScript | |
| Flat, round, designer-friendly pseudo-3D engine for canvas & SVG | ||||||||||
| 3d Machine Learning | 8,647 | 8 months ago | 19 | |||||||
| A resource repository for 3D machine learning | ||||||||||
| Physijs | 2,517 | 58 | a year ago | February 22, 2021 | 150 | mit | JavaScript | |||
| Physics plugin for Three.js | ||||||||||
| Awesome Implicit Representations | 1,795 | a year ago | 6 | mit | ||||||
| A curated list of resources on implicit neural representations. | ||||||||||
| Eos | 1,781 |  | 1 | 1 | 5 months ago | 19 | April 09, 2022 | 36 | apache-2.0 | C++ |
| A lightweight 3D Morphable Face Model library in modern C++ | ||||||||||
| Sketch Isometric | 1,656 | 4 years ago | 3 | JavaScript | ||||||
| Generate Isometric and 3D Rotation views from Artboards and Rectangles in Sketch app. | ||||||||||
| Batchgenerators | 985 |  | 6 | 20 | 8 days ago | 16 | May 02, 2022 | 38 | apache-2.0 | Jupyter Notebook |
| A framework for data augmentation for 2D and 3D image classification and segmentation | ||||||||||
| 3d Shape Analysis Paper List | 870 | 2 months ago | 2 | Python | ||||||
| A list of recent papers, libraries and datasets about 3D shape/scene analysis (by topics, updating). | ||||||||||
| 3dmm_cnn | 771 | 3 years ago | 8 | other | C++ | |||||
| Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network | ||||||||||
| Curated List Of Awesome 3d Morphable Model Software And Data | 664 | a year ago | ||||||||
| The idea of this list is to collect shared data and algorithms around 3D Morphable Models. You are invited to contribute to this list by adding a pull request. The original list arised from the Dagstuhl seminar on 3D Morphable Models https://www.dagstuhl.de/19102 in March 2019. | ||||||||||
3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks
Introduction
This archive contains source code for training / testing our algorithm for converting 2D sketches into 3D shapes. The code base contains two parts: the Network Part and the Fusion Part.
Network Part
The network part contains Python code for predicting depth and normal maps from input sketch images. The code uses TensorFlow framework to train and test the deep neural networks. Please read the README file within the Network folder for more details.
Fusion Part
The fusion part contains C++ code for fusing depth and normal maps to 3D shapes. Visual Studio is required to compile and run our code. Please read the README file within the Fusion folder for more details.
Other Notes
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Our code is released under GPL v3 license.
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If you would like to use our code, please cite the following paper:
Zhaoliang Lun, Matheus Gadelha, Evangelos Kalogerakis, Subhransu Maji, Rui Wang, "3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks", Proceedings of the International Conference on 3D Vision (3DV) 2017
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For any questions or comments, please contact Zhaoliang Lun ([email protected])
