Incorrect normalization for depth network

[dominikandreas]

Hey, just wanted to let you know that the input to the depth network for the odometry evaluation is incorrectly normalized. The depth encoder already includes normalization, so the to_color_normalized function should not be applied to the inputs for the depth network, e.g. as done here
https://github.com/TRI-ML/KP3D/blob/master/kp3d/evaluation/evaluate_keypoint_odometry.py#L71-L83

also note that this function multiplies by the standard deviation instead of dividing by it. this is probably suboptimal, but I guess since it's trained that way, it shouldn't be fixed (at least not for the pretrained keypoint net).

KP3D/kp3d/utils/image.py

Lines 112 to 115 in 3eb9d25

	assert len(images.shape) == 4
	images -= 0.5
	images *= 0.225
	return images

using the proper normalization for the depth network slightly improves the results