xinfushe/bs-net

Boundary-induced and scene-aggregated network for monocular depth prediction

Created by Junfeng Cao, Fei Sheng and Feng Xue.

Introduction

Monocular depth prediction is an important task in scene understanding. It aims to predict the dense depth of a single RGB image. Furthermore, it can be used for scene understanding and perception, such as object detection, segmentation, and 3D reconstruction. Obtaining accurate depth information is a prerequisite for many computer vision tasks.

The Data Preparation and Evaluation are following Junjie Hu with his work. Thanks for his valuable work.

Citation

If you find BS-Net useful in your research, please consider citing:

@article{BSNet,
title = {Boundary-induced and scene-aggregated network for monocular depth prediction},
author = {Feng Xue and Junfeng Cao and Yu Zhou and Fei Sheng and Yankai Wang and Anlong Ming},
journal = {Pattern Recognition},
pages = {107901},
year = {2021}
}

Dependencies

python 3.6
Pytorch 1.0
scipy 1.2.1
h5py 3.0.0
Pillow 6.0.0
scikit-image 0.17.2
scikit-learn 0.22.1

Data Preparation

NYUD v2

You may download the dataset from NYUD v2 and unzip it to the ./data folder. You will have the following directory structure:

BS-Net
|_ data
|   |_ nyu2_train
|   |_ nyu2_test
|   |_ nyu2_train.csv
|   |_ nyu2_test.csv

iBims-1

For iBims-1 dataset only have 100 RGB-D pictures especially designed for testing single-image depth estimation methods, you may download the dataset original images from iBims-1 . And you will have the following directory structure:

BS-Net
|_ data
|  |_ iBims1
|     |_ ibims1_core_raw
|     |_ ibims1_core_mat
|     |_ imagelist.txt

Training

For training BS-Net on NYUD v2 training dataset, you can run:

python train.py

You can download our trained model from Google Drive or Baidu Netdisk (Code: 1jmz).

Note that: the performance of the given model is slightly different from the manuscript, which is represented as follows. means smaller is better, and means larger is better.

Evaluation

For testing BS-Net on NYUD v2 testing dataset, you can run:

python test.py

or testing on iBims-1 dataset you can run:

python test_iBims1.py