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JamieWatson683 avatar JamieWatson683 commented on May 25, 2024

Hi - thanks for your interest, and sorry for the delayed response.

Good catch about the disabling of loading depths, that is unintentional! Thankfully it doesn't affect the training of Manydepth, but will push a fix shortly.

I am not familiar with the Lyft dataset so I am probably not the best source of information - however I believe that KITTI velodyne data has 4 values per point (z, x, y, reflectance), and that is why it is reshaped into (num_points x 4) in line16 of load_velodyne_points. Do you know how the Lyft lidar data is stored? If it is stored in some other format then you will need to amend this function accordingly.

from manydepth.

didriksg avatar didriksg commented on May 25, 2024

Hey, I checked out the Lyft lidar format. Apparently, they store their lidar data as (x, y, z, intensity, ring_index). Here's their code for reading a .bin file containing the points: https://github.com/lyft/nuscenes-devkit/blob/8b55159e89d6318f143bd44dbdfde99ad7ff72e8/lyft_dataset_sdk/utils/data_classes.py#L259-L284

The output from this reading is a (4, n_points) array in (x, y, z, intensity) format.

I see that they also have code for generating a depth map, which is what I need: https://github.com/lyft/nuscenes-devkit/blob/8b55159e89d6318f143bd44dbdfde99ad7ff72e8/lyft_dataset_sdk/lyftdataset.py#L736-L798

What I can probably do, is to generate these maps offline and load them in the dataloader. I will try it and come back to you with an update on the result.

from manydepth.

didriksg avatar didriksg commented on May 25, 2024

Hi again! I was able to generate depth maps for the Lyft data, which can be loaded in the data loader directly. However, I noticed that the compute_depth_losses function in trainer.py only seems to support depth maps in the same dimension as data from Kitti due to the cropping done here:

# garg/eigen crop
crop_mask = torch.zeros_like(mask)
crop_mask[:, :, 153:371, 44:1197] = 1
mask = mask * crop_mask

For my own training purposes, I have disabled the cropping in the compute_depth_losses function and moved the cropping out to the dataloaders, and I think it would be better overall if this cropping happens in the dataloader to support custom datasets.

from manydepth.

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