/StereoNet

A customized implementation of the paper "StereoNet: guided hierarchical refinement for real-time edge-aware depth prediction"

Primary LanguagePython

Pytorch StereoNet

Customized implementation of the Stereonet guided hierarchical refinement for real-time edge-aware depth prediction

The network archtecture of StereoNet

Attention: Not accomplished yet

  1. Stereo Matching is not my main research field, and this repo is created for a homework. So maybe it's not very completed, but I have tried to make it perfect. If you need a better version, please refer to https://github.com/meteorshowers/StereoNet.
  2. The approach of computing the cost volume in the StereoNet paper is subtracting the padding image and the other image. Here I changed it to concatenate the two images. If you want to change it to the paper's way, just set it when you initialize the net.
  3. Only training and testing on the KITTI 2015 train dataset is not enough, the best performance has achieved 74.5% (pixels with error smaller than 1). After pretraining on SceneFlow and finetune on KITTI15, the acc achieves 90.054%, not as good as the acc in paper. I have try hard to achieve the accuracy in paper, but still can't. Maybe some details are wrong.

Experiment Results till now

  1. train and test on SceneFlow datasets:
    • epoch 22 total training loss = 3.956
    • average test EPE = 3.496
  2. different finetuning on kitti 15 and result
    • 300 epochs, max 3 pixel error rate = 80.893 on kitti val 
      optimizer = RMSprop(model.parameters(), lr=1e-3, weight_decay=0.0001)
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
    • 300 epochs, max 3 pixel error rate = 83.527 on kitti val 
      optimizer = optim.Adam(model.parameters(), lr=0.001, betas=(0.9, 0.999))
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
    • 300 epochs, max 3 pixel error rate = 90.054 on kitti val 
      optimizer = optim.Adam(model.parameters(), lr=0.001, betas=(0.9, 0.999))
if epoch <= 200:
    lr = 0.001
else:
    lr = 0.0001
    • 2000 epochs, max 3 pixel error rate = 93.680 on kitti val, after 4.98 hours finetune 
      optimizer = optim.Adam(model.parameters(), lr=0.001, betas=(0.9, 0.999))
if epoch <= 200:
    lr = 0.001
else:
    lr = 0.0001

Pre-requirement

  • Pytorch 1.0.0
  • CUDA Toolkit 10
  • numpy

Datasets:

  1. Pretrain: SceneFlow
  2. KITTI 2015

You can use the anaconda virtual environment to quick start

Install Anaconda

1. wget https://repo.anaconda.com/archive/Anaconda3-5.3.1-Linux-x86_64.sh
2. bash Anaconda3-5.3.1-Linux-x86_64.sh

Please reference to Ubuntu系统下Anaconda使用方法总结 for more information about conda installation.

Create Virtual Environment according to my environment index

conda env create -n your_env_name -f environment.yaml

Training and Test

Switch to the correct python environment

conda activate your_env_name

Start training and test

Pretrain on SceneFlow dataset

cd pretrain-sceneflow
python sceneflow-pretrain.py

Finetune on KITTI 2015

cd finetune-kitti15
python finetune-kitti15.py

Coding Reference