DCL-DepthSynthesis (DCL)

Unpaired Depth Synthesis Using Differential Contrastive Learning

We provide our PyTorch implementation of the paper 'Differential Contrastive Learning for Geometry-Aware Depth Synthesis.' By proposed differential contrastive learning, our method can generate geometry-preserved depth maps comparing to previous methods.

Prerequisites

• Linux (Ubuntu is suggested)
• Python 3
• NVIDIA GPU, CUDA and CuDNN

Requirements

• pytorch >= 1.4 (for loading our pre-trained checkpoints, pytorch >= 1.8 is required.)
• tensorboardX
• numpy, h5py, opencv-python

Usage

Evaluation on depth synthesis

We provide our pre-trained checkpoints for depth synthesis and sampled some data for evaluation. The outputs can be found in ./DepthSynthesis/datasets/eval_outputs/

cd DepthSynthesis
python evaluation_demo.py

Evaluation on depth synthesis

We provide our pre-trained checkpoints for Monet-to-photo translation and sampled some data for evaluation. The outputs can be found in ./DepthSynthesis/datasets/eval_outputs/

cd DepthSynthesis
python evaluation_demo.py --name monet2photo --dataset monet2photo --input_nc 3 --output_nc 3 --ngf 64 --ndf 64 --netG resnet_9blocks --normG instance --normD instance --zoom_out_scale 0 --eval_dir ./datasets/eval_monet/

Training on depth synthesis

1. Download InteriorNet and ScanNet datasets.

2. Extract depth and rgb frames from two datasets.

3. Start training

   cd DepthSynthesis
   python train.py --dataset depthsynthesis --data_path_clean $your_path_to_interiornet --data_path_noise $your_path_to_scannet

Training on RGB image translation

1. Download datasets follow this.

2. Start training

   cd DepthSynthesis
   python train.py --dataset $RGB_dataset(for example: horse2zebra) --data_path_image your_path_to_RGB_datasets --input_nc 3 --output_nc 3

Sampled Results

Depth synthesis

CycleGAN, CUT and Coupled are previous works.

RGB image translation

Acknowledgments

Our code is developed based on contrastive-unpaired-translation. We also thank Synchronized-BatchNorm-PyTorch for synchronized batchnorm implementation.