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ScaleFlow++

Code for: ScaleFlow++: Robust and Accurate Estimation of 3D Motion from Monocular Camera https://arxiv.org/abs/2407.09797

There is a preliminary version of this work https://dl.acm.org/doi/abs/10.1145/3503161.3547979

PS: Can be found by name on Google Scholar, if ACM is not convenient to read

Requirements

The code has been tested with PyTorch 2.0.1 and Cuda 11.8.

conda create -n cscv python=3.9
conda activate cscv
pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118

Also need to install via pip

pip install matplotlib==3.5
pip install opencv-python
pip install tqdm
pip install pypng
pip install scipy
pip install einops
pip install tensorboard

Demo for ScaleFlow++

First, download the weights (Demo_Scaleflowpp.pth) from https://drive.google.com/drive/folders/129lbJWkcMwxispcRVXOvUGF12GuHbhX3?usp=drive_link and place it in the checkpoints path.

You need to specify the image path and output path in the Demo.py file (line 92,93,94)

        path1 = '/home/lh/CSCV/00026.jpg'
        path2 = '/home/lh/CSCV/00027.jpg'
        outpath = '/home/lh/CSCV/output'

CUDA_VISIBLE_DEVICES=0 python Demo_ScaleFlowpp.py --model=/home/lh/CSCV/checkpoints/Demo_Scaleflowpp.pth --mixed_precision --start=0

Demo for scaleflow

First, download the weights (Demo.pth) from https://drive.google.com/drive/folders/129lbJWkcMwxispcRVXOvUGF12GuHbhX3?usp=drive_link and place it in the checkpoints path.

You need to specify the image path and output path in the Demo.py file (line 92,93,94)

        path1 = '/home/lh/CSCV/00026.jpg'
        path2 = '/home/lh/CSCV/00027.jpg'
        outpath = '/home/lh/CSCV/output'

CUDA_VISIBLE_DEVICES=0 python Demo.py --model=/home/lh/CSCV/checkpoints/Demo.pth --mixed_precision --start=0

soapbox:

https://github.com/HanLingsgjk/CSCV/assets/102562963/14c603dd-6690-41e5-9259-025240395da8

motorbike:

https://github.com/HanLingsgjk/CSCV/assets/102562963/ea7be7b6-963d-4f4f-923b-7b1c56a0b0cb

motocross-jump:

https://github.com/HanLingsgjk/CSCV/assets/102562963/6d2f4db7-e6a6-4528-8133-2b4f64420e8e

car-shadow:

https://github.com/HanLingsgjk/CSCV/assets/102562963/c9ab1122-1d0b-4cd8-80bf-4ef9a354ab35

breakdance-flare:

https://github.com/HanLingsgjk/CSCV/assets/102562963/44f7bd1f-6286-4f2b-b117-21272fbad255

Dog:

https://github.com/HanLingsgjk/CSCV/assets/102562963/5f206891-1436-43e5-b5e1-754604eeee70

Dataset Configuration

To evaluate/train CSCV, you will need to download the required datasets.

We recommend manually specifying the path in dataset_exp_orin.py , like in line 477 def __init__(self, aug_params=None, split='kitti_test', root='/new_data/datasets/KITTI/',get_depth=0): , '/new_data/datasets/KITTI/' is where you put the KITTI dataset.

You can create symbolic links to wherever the datasets were downloaded in the datasets folder

├── datasets
    ├── KITTI
        ├── testing
        ├── training
        ├── devkit
    ├── FlyingThings3D
        ├── frames_cleanpass
        ├── frames_finalpass
        ├── optical_flow

Pretrained weights for KITTI

Download and place in the checkpoints directory ../CSCV/checkpotins/

Train ScaleFlow++ on KITTI

CUDA_VISIBLE_DEVICES=0,1 python train_scaleflowpp.py --name ScaleFlowpp --stage kitti --validation kitti --gpus 0 1 --num_steps 60000 --batch_size 6 --lr 0.000125 --image_size 320 896 --wdecay 0.0001 --gamma=0.85

Test and submit ScaleFlow++ on KITTI

Reproduce the results of Table 3 in the paper (https://arxiv.org/abs/2407.09797)

CUDA_VISIBLE_DEVICES=0 python dc_flow_eval.py --model=../CSCV/checkpotins/ResScale_KITTI160FT.pth --modelused='scaleflowpp'

If you want to submit test results to KITTI and Reproduce the results of Table 4 in the paper (https://arxiv.org/abs/2407.09797)

Of course, you need to indicate the location of the corresponding folder in the code

in dc_flow_eval.py line 543: test_dataset = datasets.KITTI(split='test', aug_params=None,root='/home/lh/all_datasets/kitti/testing')

in line 560,563,564

output_filename = os.path.join('/home/lh/CSCV_occ/submit_pre909/flow/', frame_id)

cv2.imwrite('%s/%s' % ('/home/lh/CSCV_occ/submit_pre909/disp_0', frame_id), disp1)

cv2.imwrite('%s/%s' % ('/home/lh/CSCV_occ/submit_pre909/disp_1', frame_id), disp2)

You also need to download the disp_ganet_testing folder from https://drive.google.com/drive/folders/129lbJWkcMwxispcRVXOvUGF12GuHbhX3?usp=drive_link and place it in the testing path (like:/home/lh/all_datasets/kitti/testing)

CUDA_VISIBLE_DEVICES=0 python dc_flow_eval.py --model=../CSCV/checkpotins/ResScale_kittift200.pth --modelused='scaleflowpp' --ifsubmit=True

Train Scaleflow on KITTI

CUDA_VISIBLE_DEVICES=0 python train.py --name raft-cscv --stage kitti --validation kitti --gpus 0 --num_steps 60000 --batch_size 2 --lr 0.000125 --image_size 320 960 --wdecay 0.0001 --gamma=0.85

Test Scaleflow on KITTI (This is slightly different from the original Scaleflow, as it uses a hybrid training method)

CUDA_VISIBLE_DEVICES=0 python dc_flow_eval.py --model=../CSCV/checkpotins/cscv_kitti_42.08.pth --modelused='scaleflow'