Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks

Overview

Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks

Contributions

  • A novel pairwise feature LSP to extract structural information, which is beneficial for accurate matching especially when the illumination of the image pair is imbalanced
  • A novel disparity refinement method CSR (or DSR to save memory) to deal with outliers that are difficult to match, e.g. disparity discontinuities and occluded regions.

Dependencies:

Training on SceneFlow

python train.py --data_path (your Scene Flow data folder)

Finetuning on KITTI

python KITTI_ft.py --data_path (your KITTI training data folder) --load_path (the path of the model trained on SceneFlow)
Comments
  • Testing/evaluation phase

    Testing/evaluation phase

    Good evening, I just managed to finish the training on SceneFlow and finetuning step by using the training images of KITTI_2015 and i was wondering how to evaluate the model againist the testing images. Could you give me any tips? Should i run the test_kitti.py? If so, do i need to provide the checkpoint.tar generated in the finetuning step?

    opened by Salvatore-tech 4
  • Dataloading error

    Dataloading error

    Hi, i'm trying to train the network on KITTI2015 dataset available at the following link https://s3.eu-central-1.amazonaws.com/avg-kitti/data_scene_flow.zip but i'm getting an error while loading the images.

    I'm attaching the log below, can you clarify if the dataset structure should be changed? Thanks in advance

    File "train.py", line 40, in all_limg, all_rimg, all_ldisp, test_limg, test_rimg, test_ldisp = sf.sf_loader(args.data_path) File "/home/s.starace/Lac-GwcNet-main/dataloader/sceneflow_loader.py", line 32, in sf_loader monkaa_img = filepath + [x for x in image if 'monkaa' in x][0] IndexError: list index out of range

    opened by Salvatore-tech 4
  • Tensor pushing from GPU to CPU

    Tensor pushing from GPU to CPU

    Good evening, I'm bit struggling with the storing place of tensors, in fact when running python KITTI_ft.py --data_path /home/s.starace/Dataset/KITTI_2015/training \ --load_path ./preTrained/SceneFlow.pth
    It suppose that tensors should be pushed first on the CPU before converting them through numpy... could you confirm that? Thanks in advance tensorErrorLog.txt

    opened by Salvatore-tech 3
  • test on Oxford RobotCar

    test on Oxford RobotCar

    Hi, I have tested on the stereo images of Oxford RobotCar with pretrained model kitti2015.pth directly. However, the output disparity images have some white or black patches with extremely unsmooth grayscale values, and some parts of the building have insignificant differences in disparity values. Have you done any tests on the robotcar dataset? Is there any parameter adjustment that can optimize the above problem?

    opened by XC-Young 2
  • some detail question

    some detail question

    can i have your anothor contact details. I have send your email message, if convenient, please give me a reply! Thanks very much for your good work and reply in a busy time~

    opened by luyao-cv 4
  • kitti_15 results reproduction D1 is not align with the open weights

    kitti_15 results reproduction D1 is not align with the open weights

    thanks for your good work, but i have downloaded the sceneflow weights, and trained the kitti_15 datasets using the kitti_ft.py as the command "python KITTI_ft.py --data_path /data/kitti_2015/training/ --gpu_id 4 --load_path SceneFlow.pth --no_cuda --batch_size 4" , however, the results is not good as the open weights. can you provide the command to get the results as good as the open weight? Thanks very much!

    opened by luyao-cv 1
  • About test code?

    About test code?

    Hello, thank you very much for your code, I would like to ask why you need to input the , gt_tensor to predict the display when testing?

    with torch.no_grad():
        pred_disp = model(limg_tensor, rimg_tensor, gt_tensor)
    
    opened by BreezeJing 1
Owner
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