From d3531d10f0784c1eb84088e2a950c12acb00ddb4 Mon Sep 17 00:00:00 2001 From: Spiros Thermos Date: Wed, 4 Sep 2019 15:08:28 +0300 Subject: [PATCH] models branch, index.md updated --- README.md | 65 ++----------------------------------------------------- 1 file changed, 2 insertions(+), 63 deletions(-) diff --git a/README.md b/README.md index 2d02b76..907997b 100644 --- a/README.md +++ b/README.md @@ -1,66 +1,5 @@ # Self-supervised Deep Depth Denoising -Created by [Vladimiros Sterzentsenko](https://github.com/vladsterz)__\*__, [Leonidas Saroglou](https://www.iti.gr/iti/people/Leonidas_Saroglou.html)__\*__, [Anargyros Chatzitofis](https://github.com/tofis)__\*__, [Spyridon Thermos](https://github.com/spthermo)__\*__, [Nikolaos](https://github.com/zokin) [Zioulis](https://github.com/zuru)__\*__, [Alexandros Doumanoglou](https://www.iti.gr/iti/people/Alexandros_Doumanoglou.html), [Dimitrios Zarpalas](https://www.iti.gr/iti/people/Dimitrios_Zarpalas.html), and [Petros Daras](https://www.iti.gr/iti/people/Petros_Daras.html) from the [Visual Computing Lab](https://vcl.iti.gr) @ CERTH -![poisson](./assets/images/poisson.jpg) +**Project page:*** [https://vcl3d.github.io/DeepDepthDenoising](https://vcl3d.github.io/DeepDepthDenoising) -# About this repo -This repo includes the training and evaluation scripts for the fully convolutional autoencoder presented in our paper ["Self-Supervised Deep Depth Denoising"](https://arxiv.org/pdf/1909.01193.pdf) (to appear in [ICCV 2019](http://iccv2019.thecvf.com/)). The autoencoder is trained in a self-supervised manner, exploiting RGB-D data captured by Intel RealSense D415 sensors. During inference, the model is used for depthmap denoising, without the need of RGB data. - -# Installation -The code has been tested with the following setup: - * Pytorch 1.0.1 - * Python 3.7.2 - * CUDA 9.1 - * [Visdom](https://github.com/facebookresearch/visdom) - -# Model Architecture - -![network](./assets/images/network.png) - -**Encoder**: 9 CONV layers, input is downsampled 3 times prior to the latent space, number of channels doubled after each downsampling. - -**Bottleneck**: 2 residual blocks, ELU-CONV-ELU-CONV structure, pre-activation. - -**Decoder**: 9 CONV layers, input is upsampled 3 times using interpolation followed by a CONV layer. - -# Train -To see the available training parameters: - -```python train.py -h``` - -Training example: - -```python train.py --batchsize 2 --epochs 20 --lr 0.00002 --visdom --visdom_iters 500 --disp_iters 10 --train_path /path/to/train/set``` - -# Inference -Download a pretrained model from [here](https://drive.google.com/drive/folders/15HIJrHiuqfE37v0_d-m-k5RP8UJJXmvm?usp=sharing) - * ddd --> trained with multi-view supervision (as presented in the paper): - * ddd_ae --> same model architecture, no multi-view supervision (for comparison purposes) - -To denoise a RealSense sample using a pretrained model: - -```python inference.py --model_path /path/to/pretrained/model --input_path /path/to/noisy/sample --output_path /path/to/save/denoised/sample``` - -In order to save the input (noisy) and the output (denoised) samples as pointclouds add the following flag to the inference script execution: - -```--pointclouds True``` - -To denoise a sample using the pretrained autoencoder (same model trained without splatting) add the following flag to the inference script (and make sure you load the "ddd_ae" model): - -```--autoencoder True``` - -**Benchmarking:** the mean inference time on a GeForce GTX 1080 GPU is **11ms**. - -# Citation -If you use this code and/or models, please cite the following: -``` -@inproceedings{sterzentsenko2019denoising, - author = "Vladimiros Sterzentsenko and Leonidas Saroglou and Anargyros Chatzitofis and Spyridon Thermos and Nikolaos Zioulis and Alexandros Doumanoglou and Dimitrios Zarpalas and Petros Daras", - title = "Self-Supervised Deep Depth Denoising", - booktitle = "ICCV", - year = "2019" -} -``` - -# License -Our code is released under MIT License (see LICENSE file for details) +**Source code:*** [https://github.com/VCL3D/DeepDepthDenoising](https://github.com/VCL3D/DeepDepthDenoising)