b-betty / mscnns-for-monocular-depth-estimation

MSCNNS is a CNN-based approach for monocular depth estimation implemented by pytorch.

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.

1. Download The Dataset and The Train/Test Split file.
2. 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'.
3. 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.
4. Download the model in the BaiduYun disk (Link: https://pan.baidu.com/s/1U0hw58K2M0y5QE4c3hbNng password: qnv3)
5. 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.

Quantitative results

Qualitative results