IndoorGeoNet

This code is the implementation of the following paper in Tensorflow:

IndoorGeoNet: Weakly Supervised Hybrid Learning for Depth and Pose Estimation

Amirreza Farnoosh and Sarah Ostadabbas

Requirements

This code is tested on Python3.6, TensorFlow 1.1 and CUDA 8.0 on Ubuntu 16.04.

Data preparation

The following datasets are used for experiments in the paper:

RSM Hallway

MobileRGBD

You should use the following command to preprocess dataset:

python data/prepare_train_data.py --dataset_dir=/path/to/dataset/ --dataset_name= data_name --dump_root=/path/to/formatted/data/ --seq_length=5 --img_height=144 --img_width=256

For RSM Hallway dataset, the --dataset_name should be rms_hallway, and for MobileRGBD dataset, the --dataset_name should be mobileRGBD;

Training

You should run the following command for training the network:

python geonet_main.py --mode=train_rigid --dataset_dir=/path/to/formatted/data/ --checkpoint_dir=/path/to/save/ckpts/ --learning_rate=0.0002 --seq_length=5 --batch_size=4 --max_steps=150000 

Testing

Monocular Depth

Run the following command for depth predictions:

python geonet_main.py --mode=test_depth --dataset_dir=/path/to/raw/dataset/ --pose_test_seq= test_folder_name --init_ckpt_file=/path/to/trained/model/ --batch_size=1 --output_dir=/path/to/save/predictions/ --dataset_name= data_name

Camera Pose

Run the following command for pose predictions:

python geonet_main.py --mode=test_pose --dataset_dir=/path/to/raw/dataset/ --depth_test_seq= test_folder_name --init_ckpt_file=/path/to/trained/model/ --batch_size=1 --seq_length=5 --output_dir=/path/to/save/predictions/ --dataset_name= data_name

Reference

@inproceedings{amir2018indoorgeonet,
  title     = {Weakly Supervised Hybrid Learning for Depth and Pose Estimation},
  author    = {Farnoosh, Amirreza and Ostadabbas, Sarah},
  booktitle = {arxiv},
  year = {2018}
}