Awesome

RotationNet

RotationNet takes multi-view images of an object as input and jointly estimates its pose and object category.

Asako Kanezaki, Yasuyuki Matsushita and Yoshifumi Nishida. RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints. CVPR, pp.5010-5019, 2018. (pdf) (project)

News

[2018.08.10] We uploaded the scripts and pre-trained models that <span style="color:red">reproduce our BEST results on ModelNet10 and ModelNet40.</span>

[2017.02] We got the first prize at the SHREC2017 RGB-D Object-to-CAD Retrieval Contest!
[2017.02] We got the first prize at Task 1 in the SHREC2017 Large-scale 3D Shape Retrieval from ShapeNet Core55 Challenge!
Please see SHREC2017_track3 repository to reproduce our results on SHREC2017 track3.

Requirement

1. Prepare caffe-rotationnet2

$ git clone https://github.com/kanezaki/caffe-rotationnet2.git  
$ cd caffe-rotationnet2  

Prepare your Makefile.config and compile.

$ make; make pycaffe

2. Download scripts

$ git clone https://github.com/kanezaki/rotationnet.git  
$ cd rotationnet

3. Download pre-trained models

Models trained on ModelNet10 and ModelNet40 (full set)

$ wget https://data.airc.aist.go.jp/kanezaki.asako/pretrained_models/rotationnet_modelnet10_case2_ori2.caffemodel  
$ wget https://data.airc.aist.go.jp/kanezaki.asako/pretrained_models/rotationnet_modelnet40_case2_ori4.caffemodel  

Models trained on ModelNet40 (subset)

$ wget https://data.airc.aist.go.jp/kanezaki.asako/pretrained_models/rotationnet_modelnet40_case1.caffemodel  
$ wget https://data.airc.aist.go.jp/kanezaki.asako/pretrained_models/rotationnet_modelnet40_case2.caffemodel

Getting started

Change 'caffe_root' in save_scores.py to your path to caffe-rotationnet2 repository.
Run the demo script.

$ bash demo.sh

This predicts the category of testing images. Please see below and run "demo2.sh" for testing pose estimation.

Reproduce <span style="color:red">our best</span> results on ModelNet10 and ModelNet40 (full set)

1. Download multi-view images

$ bash get_full_modelnet_png.sh  

2. Save scores and do predictions

$ bash test_full_modelnet10.sh  
$ bash test_full_modelnet40.sh  

Reproduce results on ModelNet40 (subset)

1. Download multi-view images generated in [Su et al. 2015]

$ bash get_modelnet_png.sh  

[Su et al. 2015] H. Su, S. Maji, E. Kalogerakis, E. Learned-Miller. Multi-view Convolutional Neural Networks for 3D Shape Recognition. ICCV2015.

2. Save scores and do predictions

$ bash test_modelnet40.sh  

Train your own RotationNet models

1. Download multi-view images generated in [Su et al. 2015]

$ bash get_modelnet_png.sh  

2. Download initial weights for fine-tuning the models

Please download the file "ilsvrc_2012_train_iter_310k" according to R-CNN repository
This is done by the following command:

$ wget http://www.cs.berkeley.edu/~rbg/r-cnn-release1-data.tgz  
$ tar zxvf r-cnn-release1-data.tgz  

3. Run the training operation

3-1. Case (2): Train the model w/o upright orientation (RECOMMENDED)

$ ./caffe-rotationnet2/build/tools/caffe train -solver Training/rotationnet_modelnet40_case2_solver.prototxt -weights caffe_nets/ilsvrc_2012_train_iter_310k 2>&1 | tee log.txt  

3-2. Case (1): Train the model with upright orientation

$ ./caffe-rotationnet2/build/tools/caffe train -solver Training/rotationnet_modelnet40_case1_solver.prototxt -weights caffe_nets/ilsvrc_2012_train_iter_310k 2>&1 | tee log.txt  

Test pose estimation

1. (If not done,) download multi-view images generated in [Su et al. 2015]

$ bash get_modelnet_png.sh  

[Su et al. 2015] H. Su, S. Maji, E. Kalogerakis, E. Learned-Miller. Multi-view Convolutional Neural Networks for 3D Shape Recognition. ICCV2015.

2. Align objects in the training set

$ bash make_reference_poses.sh case1 # for case (1)  
$ bash make_reference_poses.sh case2 # for case (2)   

This predicts the viewpoints of training images and writes the image file paths in the predicted order.

3. Run the demo script

$ bash demo2.sh

This predicts the category and viewpoints of testing images, and then displays 10 training objects in the predicted category seen from the predicted viewpoints.

Reproduce results on SHREC2017 track3 (Large-scale 3D Shape Retrieval from ShapeNet Core55)

Please see SHREC2017_track3 repository

License

BSD