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Multi-view CNN (MVCNN) for shape recognition
| Date | Stars |
|---|---|
| 2026-07-24 | 392 |
| 2026-07-25 | 392 |
| 2026-07-28 | 392 |
| 2026-07-30 | 392 |
| 2026-08-06 | 392 |
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# Multi-view CNN (MVCNN) for shape recognition
[Project Page](http://vis-www.cs.umass.edu/mvcnn/)
The goal of the project is to learn a general purpose descriptor for shape recognition. To do this we train discriminative models for shape recognition using convolutional neural networks (CNNs) where view-based shape representations are the only cues. Examples include **line-drawings**, **clip art images where color is removed**, or **renderings of 3D models** where there is little or no texture information present.
If you use any part of the code from this project, please cite:
@inproceedings{su15mvcnn,
author = {Hang Su and Subhransu Maji and Evangelos Kalogerakis and Erik G. Learned{-}Miller},
title = {Multi-view convolutional neural networks for 3d shape recognition},
booktitle = {Proc. ICCV},
year = {2015}}
## Other implementations
(These are implementations provided by friends or found online, and are listed here for your convenience. I do not provide direct support on them.)
* PyTorch (from my UMass labmate @jongchyisu): [mvcnn_pytorch](https://github.com/jongchyisu/mvcnn_pytorch)
* Caffe (from my UMass labmate @brotherhuang): Check out the [caffe](https://github.com/suhangpro/mvcnn/tree/master/caffe) folder
* Tensorflow (from @WeiTang114): [MVCNN-Tensorflow](https://github.com/WeiTang114/MVCNN-TensorFlow)
* Torch (from @eriche2016): [mvcnn.torch](https://github.com/eriche2016/mvcnn.torch)
* PyTorch (from @RBirkeland): [MVCNN-ResNet](https://github.com/RBirkeland/MVCNN-ResNet)
## Installation
* Install dependencies
```bash
git submodule update --init
```
* Compile
compile for CPU:
```bash
# two environment variables might need to be set, e.g. MATLABDIR=<MATLAB_ROOT> MEX=<MATLAB_ROOT>/bin/mex
matlab -nodisplay -r "setup(true);exit;"
```
compile for GPU (w/ cuDNN):
```bash
# 1) two environment variables might need to be set, e.g. MATLABDIR=<MATLAB_ROOT> MEX=<MATLAB_ROOT>/bin/mex
# 2) other compilation options (e.g. 'cudaRoot',<CUDA_ROOT>,'cudaMethod','nvcc','cudnnRoot',<CUDNN_ROOT>)
# might be needed in the 'struct(...)' as well depending on you system settings
matlab -nodisplay -r "setup(true,struct('enableGpu',true,'enableCudnn',true));exit;"
```
**Note**: you can alternatively run directly the scripts from the Matlab command window, e.g. for Windows installations:
setup(true,struct('enableGpu',true,'cudaRoot','C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0','cudaMethod','nvcc'));
You may also need to add Visual Studio's cl.exe in your PATH environment (e.g., C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\bin\amd64)
## Usage
* Extract descriptor for a shape (.off/.obj mesh). The descriptor will be saved in a .txt file (e.g. bunny_descriptor.txt). Uses default model with no fine-tuning. Assumes upright orientation by default.
```matlab
MATLAB> shape_compute_descriptor('bunny.off');
```
* Extract descriptor for all shapes in a folder (.off/.obj meshes). The descriptors will be saved in .txt files in the same folder. Assumes no upright orientation.
```matlab
MATLAB> shape_compute_descriptor('my_mesh_folder/','useUprightAssumption',false);
```
* Extract descriptors for all shapes in a folder (.off/.obj meshes) and post-process descriptors with learned metric. Uses non-default models.
```matlab
MATLAB> shape_compute_descriptor('my_mesh_folder/', 'cnnModel', 'my_cnn.mat', ...
'metricModel', 'my_metric.mat','applyMetric',true);
```
* Download datasets for training/evaluation (should be placed under data/)
* modelnet40v1 (12 views w/ upright assumption): [tarball](http://maxwell.cs.umass.edu/mvcnn-data/modelnet40v1.tar) (204M)
* modelnet40v2 (80 views w/o upright assumption): [tarball](http://maxwell.cs.umass.edu/mvcnn-data/modelnet40v2.tar) (1.3G)
* shapenet55v1 (12 views w/ upright assumption): [tarball](htExcerpt of 4,408 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7bbdd5ad21b95324, topic:deep-learning
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