What is the 'input_channels_depth' parameter for?

[rakshith95]

Hello,
In run_kbnet.py, you have an argument 'input_channels_depth' whose default is 2; I'm not sure what this means.
In networks.py, it's written that it is the "number of input channels for depth branch", but why would the depth be 2 channel?

[rakshith95]

Related, but I also cannot find the part of the code in run_kbnet where the pretrained model weights are loaded for inference.

EDIT: I see that the line depth_model.restore_model(depth_model_restore_path) in kbnet.py does this.

[alexklwong]

Hi, so input_channels_depth is 2 because we also follow the existing works to feed in not just sparse depth but also a validity map (in our case binary). It has been founded by several works that it improves performance.

run_kbnet.py takes in the argument --depth_model_restore_path
https://github.com/alexklwong/calibrated-backprojection-network/blob/master/src/run_kbnet.py#L84

which is passed to the run function here
https://github.com/alexklwong/calibrated-backprojection-network/blob/master/src/run_kbnet.py#L144

and it is restored here
https://github.com/alexklwong/calibrated-backprojection-network/blob/master/src/kbnet.py#L804

[rakshith95]

Hi, so input_channels_depth is 2 because we also follow the existing works to feed in not just sparse depth but also a validity map (in our case binary). It has been founded by several works that it improves performance.

Ah okay, thank you. Now, I see that the validity map for the sparse data during inference is obtained for positive depths here:

        validity_map_depth = torch.where(
            sparse_depth > 0,
            torch.ones_like(sparse_depth),
            sparse_depth) 

Can I know what conditions you used to generate the validity map in the original dataset? I'm not sure about XIVO but I assume all VIO algorithms filter out points behind the camera.

[alexklwong]

In the case VOID the validity map are taken as is based on the points tracked by XIVO and yes any point that is not visible in the frame is not valid. For KITTI however, the validity map and sparse point cloud may differ. Namely that we do a naive filtering to remove points that straddle occlusion boundaries.

https://github.com/alexklwong/calibrated-backprojection-network/blob/master/src/kbnet.py#L411
The parameters that we have chosen for this has no effect on VOID, but will remove points in KITTI in which case the validity map will be a subset of lidar returns.

[rakshith95]

Alright, that makes sense, thank you