models branch, index.md updated

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)