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Self-Supervised 3D Scene Flow Estimation Guided by Superpoint

This is the code for SPFlowNet, a self-supervised 3D scene flow estimation framework from 3D point clouds with dynamic superpoints. The code is created by Yaqi Shen (syq@njust.edu.cn).

Introduction

This repository contains the source code and pre-trained models for SPFlowNet (published on CVPR 2023).

Prerequisities

Our model is trained and tested under:

• Python 3.6.6
• CUDA 10.1
• Pytorch (torch == 1.4.0)
• scipy
• tqdm
• sklearn
• numba
• cffi
• yaml
• h5py

Build the ops. We use the operation from this repo.

cd lib/pointops && python setup.py install && cd ../../

Data preprocess

FlyingThings3D

For fair comparison with previous methods, we adopt the preprocessing steps in HPLFlowNet.

Download and unzip the "Disparity", "Disparity Occlusions", "Disparity change", "Optical flow", "Flow Occlusions" for DispNet/FlowNet2.0 dataset subsets from the FlyingThings3D website (we used the paths from this file, now they added torrent downloads) . They will be upzipped into the same directory, RAW_DATA_PATH. Then run the following script for 3D reconstruction:

python data_preprocess/process_flyingthings3d_subset.py --raw_data_path RAW_DATA_PATH --save_path SAVE_PATH/FlyingThings3D_subset_processed_35m --only_save_near_pts

This dataset is denoted FT3D_s in our paper.

KITTI Scene Flow 2015

• KITTI_s dataset

For fair comparison with previous methods, we adopt the preprocessing steps in HPLFlowNet.

Download and unzip KITTI Scene Flow Evaluation 2015 to directory RAW_DATA_PATH. Run the following script for 3D reconstruction:

python data_preprocess/process_kitti.py RAW_DATA_PATH SAVE_PATH/KITTI_processed_occ_final

This dataset is denoted KITTI_s in our paper.

• KITTI_o dataset

For fair comparison with previous methods, we adopt the preprocessing steps in FlowNet3D.

Download and unzip data processed by FlowNet3D to directory SAVE_PATH. This dataset is denoted KITTI_o in our paper.

• KITTI_r dataset

Following RigidFlow, we also use raw data from KITTI for self-supervised scene flow learning. The unlabeled training data provided by RigidFlow can be found in here. This dataset is denoted KITTI_r in our paper.

Get started

Here are some demo results in KITTI dataset:

<img src="./figure/inputs.gif" width=30%> <img src="./figure/superpoints.gif" width=30%> <img src="./figure/EPE.gif" width=30%>

Train

Train on non-occluded data

Set data_root in config_train_FT3D.yaml to SAVE_PATH in the data preprocess section. Then run

python train_FT3D.py ./configs_without_occlusions/config_train_FT3D.yaml

Train on occluded data

Similarly, specify data_root in config_train_KITTI_r.yaml. Then run

python train_KITTI_r.py  ./configs_with_occlusions/config_train_KITTI_r.yaml

Evaluate

We upload our pretrained models in pretrained.

Evaluate on non-occluded data

Set data_root in config_evaluate.yaml to SAVE_PATH in the data preprocess section, and specify dataset in the configuration file config_evaluate.yaml. Then run

python evaluate.py ./configs_without_occlusions/config_evaluate.yaml

Evaluate on occluded data

Set data_root and dataset in the configuration file config_evaluate_occ.yaml. Then run

python evaluate_occ.py ./configs_with_occlusions/config_evaluate_occ.yaml

SPFlowNet-V2

We redefine the encoding of current iteration information in our flow refinement module, which can achieve better experimental results (model_v2.py).

FT3d_s dataset

KITTI_s dataset

KITTI_o dataset

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{shen2023self,
  title={Self-Supervised 3D Scene Flow Estimation Guided by Superpoints},
  author={Shen, Yaqi and Hui, Le and Xie, Jin and Yang, Jian},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5271--5280},
  year={2023}
}

Acknowledgement

Our code refers to flownet3d_pytorch, FLOT, PointPWC, HPLFlowNet, FlowStep3D, RigidFlow, and SPNet. We want to thank the above open-source projects.