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Context: We train our system on the split provided by [7], and exclude all the frames from the testing scenes as well as static sequences with mean optical flow magnitude less than 1 pixel for training. We fix the length of image sequences to be 3 frames, and treat the central frame as the target view and the \pm 1 frames as the source views. We use images captured by both color cameras, but treated them independently when forming training sequences. This results in a total of 44,540 sequences, out of which we use 40,109 for training and 4,431 for validation. We show sample predictions made by our initial Cityscapes model and the final model (pre-trained on Cityscapes and then fine-tuned on KITTI) in Figure 7. Due to the domain gap between the two datasets, our Cityscapes model sometimes has difficulty in recovering the complete shape of the car/bushes, and mistakes them with distant objects.

Question: What type of scenes were used for training?
Proposed model is trained on the Cityscapes dataset and then fine tuned on KITTI scenes. The training split used is from [7]. This exclude all the frames from the testing scenes as well as static sequences with mean optical flow magnitude less than 1 pixel for training. This results in a total of 44,540 sequences, out of which we use 40,109 for training and 4,431 for validation.