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Demo code for ICCV19 Paper: SANet: Scene Agnostic Network for Camera Localization

Requirements

• CUDA 9.0
• OpenCV 3.2
• pybind11 2.4.3
• Python 3.6
• Pytorch 0.4.1 (The cuda module requires 0.4.1 to compile)
• jupyter lab or jupyter notebook
• ipyvolume 0.6.0 (Visualizing 3D point cloud in jupyter)

Preparation

1. Download 7Scenes dataset from https://www.microsoft.com/en-us/research/project/rgb-d-dataset-7-scenes/
2. extract all sequences zip files.
3. Re-organize the sequences using:

   python seq_data/seven_scenes/scenes2seq.py <7scene_seq_dir>
   
   e.g. suppose 7scenes sequence `heads` in /home/xxx/Dcouemtns/7scenes/heads, then,
   python seq_data/seven_scenes/scenes2seq.py /home/xxx/Dcouemtns/7scenes/heads

   The above python script will generate two binary files: train_frames.bin and train_frames.bin inside of sequence directory, each file stores information of train or test frames (e.g. extrinsic and intrinsic matrix), and can be loaded wih pickle lib.
4. Download pre-trained pytorch model from Google Drive, unzip files to data folder. It has two pre-trained model:
   • seven_scene_model.pth sa-net model trained with sun3d and use 7Scenes sequences for evaluation.
   • netvlad_vgg16.tar netvlad model used for query retrieval.
5. Compile python interface for vislearn/LessMore:

   cd libs/lm_pnp
   mkdir build
   cd build
   cmake ..
   make all

   Note: you may need to modify the variable PYTHON_EXECUTABLE in line 59 of CMakeLists.txt with your own python interpreter.
6. Compile PointNet++ module (requires pytorch 0.4.1)

   cd relocal/pointnet2
   mkdir build
   cd build
   cmake ..
   make

Example (7Scenes)

Please refer to notebook example_7scenes.ipynb, it requires ipyvolume lib for visualizing point clouds.

Todo:

Add cambridge dataset examples.

References:

1. E. Brachmann, C. Rother, ”Learning Less is More - 6D Camera Localization via 3D Surface Regression”, CVPR 2018
2. Charles R. Qi, Li (Eric) Yi, Hao Su, Leonidas J. Guibas, "PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space"