Decoder Modulation for Indoor Depth Completion

Decoder Modulation for Indoor Depth Completion
Dmitry Senushkin, Ilia Belikov, Anton Konushin
Samsung AI Center Moscow
https://arxiv.org/abs/2005.08607

Abstract: Accurate depth map estimation is an essential step in scene spatial mapping for AR applications and 3D modeling. Current depth sensors provide time-synchronized depth and color images in real-time, but have limited range and suffer from missing and erroneous depth values on transparent or glossy surfaces. We investigate the task of depth completion that aims at improving the accuracy of depth measurements and recovering the missing depth values using additional information from corresponding color images. Surprisingly, we find that a simple baseline model based on modern encoder-decoder architecture for semantic segmentation achieves state-of-the-art accuracy on standard depth completion benchmarks. Then, we show that the accuracy can be further improved by taking into account a mask of missing depth values. The main contributions of our work are two-fold. First, we propose a modified decoder architecture, where features from raw depth and color are modulated by features from the mask via Spatially-Adaptive Denormalization (SPADE). Second, we introduce a new loss function for depth estimation based on direct comparison of log depth prediction with ground truth values. The resulting model outperforms current state-of-the-art by a large margin on the challenging Matterport3D dataset.

Installation

This implementation is based on Python 3+ and Pytorch 1.4+. We provide two ways of setting up an environment. If you are using Anaconda, the following code performs necessary installation:

conda env create -f environment.yaml
conda activate depth-completion
python setup.py install

The same procedure can be done with pip:

pip3 install -r requirements.txt
python setup.py install

Training

We provide a code for training on Matterport3D. Download Matterpord3D dataset and reorder your root folder as follows:

ROOT/
  ├── data/
  └── splits/
        ├── train.txt
        ├── val.txt
        └── test.txt

and data directory is should be configured in this order. Be sure that ROOT path in matterport.py is valid. Now you can start training with the following command:

# for LRN decoder with efficientnet-b4 backbone
python train_matterport.py --default_cfg='LRN' --config_file='../configs/LRN_efficientnet-b4_lena.yaml' --postfix='example_lrn' 
# for DM-LRN decoder with efficientnet-b4 backbone
python train_matterport.py --default_cfg='DM-LRN' --config_file='../configs/DM-LRN_efficientnet-b4_pepper.yaml' --postfix='example_dm_lrn'

Evaluation

We provide scripts for evaluation on Matterport3D. If you need to perform test on NYUv2, see directly into a code since it may be changed in the future. Following instructions performs evaluation on Matterport3D test set:

# for LRN decoder with efficientnet-b4 backbone
python test_net.py --default_cfg='LRN' --config_file='../configs/LRN_efficientnet-b4_lena.yaml' --weights=<path to lrn_b4.pth>
# for DM-LRN decoder with efficientnet-b4 backbone
python test_net.py --default_cfg='DM-LRN' --config_file='../configs/DM-LRN_efficientnet-b4_pepper.yaml' --weights=<path to dm-lrn_b4.pth>
# if you need to visualize the results just add --save_dir argument
python test_net.py --default_cfg='DM-LRN' --config_file='../configs/DM-LRN_efficientnet-b4_pepper.yaml' --weights=<path to dm-lrn_b4.pth> --save_dir=<path to existing folder>

Model ZOO

This repository includes all models mentioned in original paper.

Backbone	Decoder type	Encoder input	Training loss	Link	Config
efficientnet-b0	LRN	RGBD	LogDepthL1loss	lrn_b0.pth	LRN_efficientnet-b0_suzy.yaml
efficientnet-b1	LRN	RGBD	LogDepthL1loss	lrn_b1.pth	LRN_efficientnet-b1_anabel.yaml
efficientnet-b2	LRN	RGBD	LogDepthL1loss	lrn_b2.pth	LRN_efficientnet-b2_irina.yaml
efficientnet-b3	LRN	RGBD	LogDepthL1loss	lrn_b3.pth	LRN_efficientnet-b3_sara.yaml
efficientnet-b4	LRN	RGBD	LogDepthL1loss	lrn_b4.pth	LRN_efficientnet-b4_lena.yaml
efficientnet-b4	LRN	RGBD	BerHu	lrn_b4_berhu.pth	LRN_efficientnet-b4_helga.yaml
efficientnet-b4	LRN	RGBD+M	LogDepthL1loss	lrn_b4_mask.pth	LRN_efficientnet-b4_simona.yaml
efficientnet-b0	DM-LRN	RGBD	LogDepthL1Loss	dm-lrn_b0.pth	DM_LRN_efficientnet-b0_camila.yaml
efficientnet-b1	DM-LRN	RGBD	LogDepthL1Loss	dm-lrn_b1.pth	DM_LRN_efficientnet-b1_pamela.yaml
efficientnet-b2	DM-LRN	RGBD	LogDepthL1Loss	dm-lrn_b2.pth	DM_LRN_efficientnet-b2_rosaline.yaml
efficientnet-b3	DM-LRN	RGBD	LogDepthL1Loss	dm-lrn_b3.pth	DM_LRN_efficientnet-b3_jenifer.yaml
efficientnet-b4	DM-LRN	RGBD	LogDepthL1Loss	dm-lrn_b4.pth	DM_LRN_efficientnet-b4_pepper.yaml
efficientnet-b4	DM-LRN	RGBD	BerHu	dm-lrn_b4_berhu.pth	DM_LRN_efficientnet-b4_amelia.yaml

License

The code is released under the MPL 2.0 License. MPL is a copyleft license that is easy to comply with. You must make the source code for any of your changes available under MPL, but you can combine the MPL software with proprietary code, as long as you keep the MPL code in separate files.

Citation

If you find this work is useful for your research, please cite our paper:

@article{dmidc2020,
  title={Decoder Modulation for Indoor Depth Completion},
  author={Dmitry Senushkin, Ilia Belikov, Anton Konushin},
  journal={arXiv preprint arXiv:2005.08607},
  year={2020}
}