Where do you use mono_depth?

[mks0601]

Hi, thanks for your amazing work.
I was wondering where do you use depthmaps from mono_depth.
I can't find it in both of your pre-processing files including 1) https://github.com/apple/ml-neuman/blob/main/preprocess/export_alignment.py and 2) https://github.com/apple/ml-neuman/blob/main/preprocess/optimize_smpl.py.
Could you clarify where and how do you use the depthmaps from mono_depth?

[jiangwei221]

The depth values are used for regularizing the background nerf, and you can find it inside the dataloader:

ml-neuman/datasets/background_rays.py

Line 51 in 0149d25

depths_list = [] # MVS/fused depth values

[mks0601]

Hi, I backtraced your code, and it seems you're not using mono_depth, but only use depth_maps.

1. Read depth data with from this code (https://github.com/apple/ml-neuman/blob/0149d258b2afe6ef65c91557bba9f874675871e4/train.py#L37C26-L37C26).
2. read_data_to_ram can be found in here:

   ml-neuman/scenes/scene.py

   Line 24 in 0149d25

   def read_data_to_ram(self, data_list=['image']):

3. read_data_to_ram function calls read_image_to_ram and read_depth_to_ram function for each cap, where cap is an instance of this class (

   ml-neuman/data_io/neuman_helper.py

   Line 22 in 0149d25

   class NeuManCapture(captures_module.RigRGBDPinholeCapture):

   )
4. read_image_to_ram first reads mono_depth with this code (

   ml-neuman/data_io/neuman_helper.py

   Line 52 in 0149d25

   return self.captured_image.read_image_to_ram() + self.captured_mask.read_image_to_ram() + self.captured_mono_depth.read_depth_to_ram()

   )
5. But it overwrites the depth data with read_depth_to_ram again from the above 3rd stage, where the read_depth_to_ram calls this function (

   ml-neuman/cameras/contents.py

   Line 110 in 0149d25

   def read_depth(self):

   ).
6. As captured_depth is defined with depth_path, not mono_depth_path, it overwrites mono_depth data of the above 4th stage with depth data of the 5th stage. FYI, depth_path refers to MVS depth data.

Could you check am I right? Thanks!

[jiangwei221]

Sorry for the over complicated pipeline... It was inherited from a sfm project.
I think what happens to mvs depth and monocular depth is that we used monocular depth to fill the holes in mvs depth maps. see:

ml-neuman/data_io/neuman_helper.py

Lines 77 to 86 in 0149d25

	@property
	def fused_depth_map(self):
	if self._fused_depth_map is None:
	valid_mask = (self.depth_map > 0) & (self.mask == 0)
	x = self.mono_depth_map[valid_mask]
	y = self.depth_map[valid_mask]
	res = scipy.stats.linregress(x, y)
	self._fused_depth_map = self.depth_map.copy()
	self._fused_depth_map[~valid_mask] = self.mono_depth_map[~valid_mask] * res.slope + res.intercept
	return self._fused_depth_map

Then the fused depth map is used to regularize the bkg nerf:

ml-neuman/datasets/background_rays.py

Line 82 in 0149d25

depths = (cap.fused_depth_map[coords[:, 1], coords[:, 0]]).astype(np.float32)

[mks0601]

Great thanks! Now I got it. BTW, are you using other geometry data, such as densepose and keypooints when training NeRF (not for preprocessing)? It seems NeuManCapture loads them but not use them

[jiangwei221]

We didn't use densepose/keypoints during nerf training stage, iirc. You can double check by setting them to None manually.

[mks0601]

Awesome. thanks!

[mks0601]

Hi jianwei221, two follow-up questions.

1. Why do you use mono_depth only for background? Why not use it for foreground?
2. Why do you adjust scale and translation only based on human area like this (

   ml-neuman/data_io/neuman_helper.py

   Line 83 in 0149d25

   res = scipy.stats.linregress(x, y)

   )? Why not use bkg area for the adjustment?

Thanks in advance!