Low performance on Monodepth2 generated depth maps

[amajee11us]

Hi, I am trying to use your work with depth maps generated from monodepth2 depth estimation network.

I tried using one of your kitti samples to test the pipeline, with depth configuration as :
conf.depth_mean = [107.0805491, 68.26778312, 133.50751215] conf.depth_std = [38.65614623, 73.59464917, 88.24401221]

Even then, the max score that i get is less than 30 percent.
Depth map and image uploaded for reference.
Is there any configurations I need to change in the monodepth code itself to get this working?

Model used for testing is the pretrained model provided in the repo.
My monodepth post processing code is:
_, scaled_depth = disp_to_depth(disp, 0.1, 100)
depth_resized = torch.nn.functional.interpolate(scaled_depth, (original_height, original_width), mode="bilinear", align_corners=False)
depth_resized_np = depth_resized.squeeze().cpu().numpy()
normalizer_depth = mpl.colors.Normalize(vmin=depth_resized_np.min())
mapper_depth = cm.ScalarMappable(norm=normalizer_depth, cmap='gray')
colormapped_im_depth = (mapper_depth.to_rgba(depth_resized_np)[:, :, :3] * 255).astype(np.uint8)
im_depth = pil.fromarray(colormapped_im_depth)

Depth Map:

Image:

Output is :(
Car -1 -1 -0.420610 690.218506 147.377106 1291.588501 441.522003 1.693756 1.490031 3.745631 2.358071 1.346878 1.293291 0.648531 0.281668
Car -1 -1 -0.314688 271.298126 136.579041 803.299255 431.537445 1.713774 1.436281 3.459033 1.215498 1.356887 1.107118 0.517339 0.234700

[dingmyu]

Hi, do you just inference with monodepth2 depth maps? You should retrain the model with depth maps generated by monodepth2.