M-FUSE: Multi-frame Fusion for Scene Flow Estimation

This repository contains the official code for our paper

M-FUSE: Multi-frame Fusion for Scene Flow Estimation
L. Mehl, A. Jahedi, J. Schmalfuss, A. Bruhn
Winter Conference on Applications of Computer Vision (WACV), 2023.

@inproceedings{Mehl2023,
  title={{M-FUSE}: Multi-frame Fusion for Scene Flow Estimation},
  author={Mehl, Lukas and Jahedi, Azin and Schmalfuss, Jenny and Bruhn, Andr{\'e}s},
  booktitle={Proc. Winter Conference on Applications of Computer Vision (WACV)},
  year={2023}
}

Code Overview:

• data_readers: code related to data handling
• mfuse: model definitions
• scripts: scripts for training, evaluation, submission

Setup

• Install all required python packages: pytorch, numpy, scipy, opencv, tqdm, scikit-sparse, pypng
• Install the lietorch package. See https://github.com/princeton-vl/lietorch for details.

The code was tested with Python 3.9, PyTorch 1.10.2, CUDA 11.6

Datasets

Download the KITTI scene flow dataset with the multi-frame extension from http://www.cvlibs.net/datasets/kitti/eval_scene_flow.php and make sure that it is located in the directory $DATASETS/kitti15 where $DATASETS is an environment variable.

Create disparity files for KITTI using the LEAStereo code https://github.com/XuelianCheng/LEAStereo and put them into $DATASETS/kitti15/training/disp_lea and $DATASETS/kitti15/testing/disp_lea respectively. You can also download precomputed results of LEAStereo for the train and testing split.

Usage

After training M-FUSE on the KITTI dataset for 50K steps, results can be evaluated using

python scripts/evaluation_fusion.py --model=<path-to-checkpoint>.pth

A submission for the KITTI benchmark can be created using

python scripts/kitti_submission_fusion.py --model=<path-to-checkpoint>.pth

Our resulting checkpoint can be downloaded here, which yields an SF-all error of 4.83 on the KITTI benchmark.

Training

1. Retrain the RAFT-3D model raft3d_bilaplacian on the FlyingThings3D dataset for 200K steps with their provided code https://github.com/princeton-vl/RAFT-3D or use their checkpoint.

2. Train our fusion model:

python scripts/train_fusion.py --ckpt_r3d=<path-to-pretrained-r3d>