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Indoor SfMLearner

PyTorch implementation of our ECCV2020 paper:

P²Net: Patch-match and Plane-regularization for Unsupervised Indoor Depth Estimation

Zehao Yu*, Lei Jin*, Shenghua Gao

(* Equal Contribution)

Getting Started

Installation

pip install -r requirements.txt

Then install pytorch with

conda install pytorch torchvision cudatoolkit=10.2 -c pytorch

Pytorch version >= 0.4.1 would work well.

Download pretrained model

Please download pretrained model from Onedrive and extract:

tar -xzvf ckpts.tar.gz 
rm ckpts.tar.gz

Prediction on single image

Run the following command to predict on a single image:

python inference_single_image.py --image_path=/path/to/image

By default, the script saves the predicted depth to the same folder

Evaluation

Download testing data from Onedrive and put to ./data.

cd data
tar -xzvf nyu_test.tar.gz 
tar -xzvf scannet_test.tar.gz
tar -xzvf scannet_pose.tar.gz
cd ../

NYUv2 Dpeth

CUDA_VISIBLE_DEVICES=1 python evaluation/nyuv2_eval_depth.py \
    --data_path ./data \
    --load_weights_folder ckpts/weights_5f \
    --post_process  

NYUv2 normal

CUDA_VISIBLE_DEVICES=1 python evaluation/nyuv2_eval_norm.py \
    --data_path ./data \
    --load_weights_folder ckpts/weights_5f \
    # --post_process

ScanNet Depth

CUDA_VISIBLE_DEVICES=1 python evaluation/scannet_eval_depth.py \                                               
    --data_path ./data/scannet_test \
    --load_weights_folder ckpts/weights_5f \
    --post_process

ScanNet Pose

CUDA_VISIBLE_DEVICES=1 python evaluation/scannet_eval_pose.py \
    --data_path ./data/scannet_pose \
    --load_weights_folder ckpts/weights_5f \
    --frame_ids 0 1

Training

First download NYU Depth V2 on the official website and unzip the raw data to DATA_PATH.

Extract Superpixel

Run the following command to extract superpixel:

python extract_superpixel.py --data_path DATA_PATH --output_dir ./data/segments

3-frames

Run the following command to train our network:

CUDA_VISIBLE_DEVICES=1 python train_geo.py \                                                                   
    --model_name 3frames \
    --data_path DATA_PATH \
    --val_path ./data \
    --segment_path ./data/segments \
    --log_dir ./logs \
    --lambda_planar_reg 0.05 \
    --batch_size 12 \
    --scales 0 \
    --frame_ids_to_train 0 -1 1

5-frames

Using the pretrained model from 3-frames setting gives better results.

CUDA_VISIBLE_DEVICES=1 python train_geo.py \                                                                   
    --model_name 5frames \
    --data_path DATA_PATH \
    --val_path ./data \
    --segment_path ./data/segments \
    --log_dir ./logs \
    --lambda_planar_reg 0.05 \
    --batch_size 12 \
    --scales 0 \
    --load_weights_folder FOLDER_OF_3FRAMES_MODEL \
    --frame_ids_to_train 0 -2 -1 1 2

Acknowledgements

This project is built upon Monodepth2. We thank authors of Monodepth2 for their great work and repo.

License

TBD

Citation

Please cite our paper for any purpose of usage.

@inproceedings{IndoorSfMLearner,
  author    = {Zehao Yu and Lei Jin and Shenghua Gao},
  title     = {P$^{2}$Net: Patch-match and Plane-regularization for Unsupervised Indoor Depth Estimation},
  booktitle = {ECCV},
  year      = {2020}
}