Official PyTorch implementation of paper:
DIP: Deep Inverse Patchmatch for High-Resolution Optical Flow, CVPR 2022
upload stereo training codes, Mainly modified from unimatch and CRE-Pytorch
Here we add stereo matching support for onnx
Our code is based on pytorch 1.6.0, CUDA 10.1 and python 3.8. The stereo matching version is based on pytorch 2.0.1, CUDA 11.7 and python 3.10.12
All pretrained models can be downloaded from google drive.
You can run a trained model on a sequence of images and visualize the results:
CUDA_VISIBLE_DEVICES=0 python demo.py \
--model DIP_sintel.pth
for stereo matching version:
CUDA_VISIBLE_DEVICES=0 python demo_stereo.py \
--model DIP_stereo.pth --path input_stereo_imgs
The datasets used to train and evaluate DIP flow are as follows:
Furthermore, we supplemented the training data with stereo image pairs from the CRE dataset to enhance the diversity of scenarios for our stereo matching model.
This project is based on RAFT and SCV. We thank the original authors for their excellent work.