Introduction for MSCNNS

MSCN_NS (Multi-scale Sub-pixel Convolutional Network with a Neighborhood Smoothness constraint) is a CNN-based approach for monocular depth estimation.

For technical details, please see this paper (comming soon).

Prerequisites

Matlab R2017a (or other proper version)
python v3.5.x
pytorch v0.3.0 (or later version)
numpy
scipy

How to test

Quick test

You may use the provided model (see the BaiduYun link below) and test samples to test this apporach as follows,

python3 test.py --model <the pytorch model> --image ./test_samples/nyu_v2_175.mat

Test on the whole NYU Depth v2 dataset.

Download The Dataset and The Train/Test Split file.
Suppose you have saved the dataset in <path_to_data> and the split file in <path_to_split>. Open matlab/gen_test_data_for_mscn.m and assign <path_to_data> to 'NYUv2_data' and <path_to_split> to 'split_file'.
run matlab/gen_test_data_for_mscn.m and the test data will be generated in '../Dataset/test'. You may change the save root 'test_root' to anywhere you like.
Download the model in the BaiduYun disk (Link: https://pan.baidu.com/s/1U0hw58K2M0y5QE4c3hbNng password: qnv3)
Test the model as follows,

python3 test.py --model <the pytorch model> --data <folder for the generated test data>

Results

Note that you may find the references and more comparisons in the aforementioned paper.

wEEang763162/MSCNNS-for-monocular-depth-estimation