Open Tiam271 opened 3 years ago
What is the range of the disparities? Are you starting from random initialization or from a set of pre trained weights?
The range of the disparities is between 0 and 192. It is starting from random initialization. Do you mean that if I start training from random initialization, I should use dense ground-truth?
Generally speaking yes it is more stable with full disparities, or at least not very sparse ones. Have you tried starting from the Flying Things 3D weights?
some additional questions:
When I start from the pre-trained weights(Flying Things 3D), this problem does not occur. I understand, this is because the supervision information is too sparse. Thank you for your answer!
I see, so the encoding of the GT seems ok. Probably as you said you are experiencing collapsing because the supervision is too sparse. I would bet that by playing with the hyperparameters you might be able to train the network directly on the sparse data, but in general I think it's beneficial to start from the F3D weights. Within the same codebase you can also experiment with Dispnet, even if it is slower it's a way more stable model and you might be able to sucesfully train it on sparse data from scratch (not sure tough)
Hello great work @AlessioTonioni and team! I am trying to use the work for training on custom data which has spare disparity maps (16 bit) as shown below:
I did not modify any code. However, as the training progresses, the disparity predicted by the network has always been like this: (Except for the first frame)
Do you know why? Can you help me?