mjitg / pytorch-hitnet-hierarchical-iterative-tile-refinement-network-for-real-time-stereo-matching Goto Github PK

Thanks a lot for your work! Are the trained models available and can you show the quantitative results to compare your implementation with the original one? Thanks ahead!

Hello Mjitg, I see that you have implemented the code according to the paper by making different submodules, but because we don't have any video demonstration of how the architecture is working we are unable to understand it, so it would be great if you could upload a video demonstrating the algorithm, it need not cover all the part, could just explain the model and loss function, that will be more than enough.

Thank you

Akanksh

Great work！ Could you show the performance of your code in terms of EPE and >npixl metric ? How close are they to the original paper?

When it runs training, it always shown this error after some iterations:

/opt/conda/conda-bld/pytorch_1565287025495/work/aten/src/THC/THCTensorScatterGather.cu:100: void THCudaTensor_gatherKernel(TensorInfo<Real, IndexType>, TensorInfo<Real, IndexType>, TensorInfo<long, IndexType>, int, IndexType) [with IndexType = unsigned int, Real = float, Dims = -1]: block: [0,0,0], thread: [6,0,0] Assertion indexValue >= 0 && indexValue < src.sizes[dim] failed.
Traceback (most recent call last):
File "/home/workspace/HITNet/PyTorch-HITNet/main.py", line 297, in
train()
File "/home/workspace/HITNet/PyTorch-HITNet/main.py", line 125, in train
loss, scalar_outputs, image_outputs = train_sample(sample, compute_metrics=do_summary)
File "/home/workspace/HITNet/PyTorch-HITNet/main.py", line 225, in train_sample
disp_gt, dx_gt, dy_gt, args.maxdisp)
File "/home/workspace/HITNet/PyTorch-HITNet/loss/total_loss.py", line 46, in global_loss
lambda_init * init_loss(cv, d_gt_pyramid[i], maxdisp)[mask]
File "/home/workspace/HITNet/PyTorch-HITNet/loss/initialization_loss.py", line 16, in init_loss
cost_nm = torch.gather(pred_init_cost, 1, get_non_match_disp(pred_init_cost, d_gt))
File "/home/workspace/HITNet/PyTorch-HITNet/loss/initialization_loss.py", line 50, in get_non_match_disp
INF = torch.Tensor([float("Inf")]).view(1, 1, 1, 1).repeat(B, D, H, W).to(d_gt.device)
RuntimeError: CUDA error: device-side assert triggered

Have you ever met this before? Or any ways to solve to this?

hi,
Can you help me explain what ts means in the class DispUpsampleBySlantedPlane ?

Thanks for your work， This is a very perfect work,
I want to used myself dataset for your code, but i want to konw how to generate Slant parameter GT, Can you share Detailed generation process, Thank you very much!!!

there's a "maxdisp" in "subpix_cost" function in "loss/initialization_loss.py", line 28 to be accurate. But it seems to be a bug? It hasn't been used as a hyperparameter, nor a passed temporal parameter.

After Kitti trained the network, the model was much less effective in inference. But it's not an overfit. Because I'm using the test set to inference.and The dataload for test and inference is the same. Does anyone have a problem with that.

Hi.
There are 2 .pfm images not existing according to the slant data you provided:
it seems 000114_10.pfm & 000115_10.pfm are missing?

Thanks for your share slant generation code, but access denied when I access the code link, I have clicked the permission request, Have you received the permission request, Thank you very much~