Hello, I implemented depth warping just using inverse_warp.py 1 from DPSNet for 480*640 image and its depth map
I want to create a binary map with the target coordinates of depth warping as 1 and the other coordinates as 0. In other words, it means displaying the occlusion-exposed background as 0 and the non-occlusion as 1. (What I want is not an occlusion mask, but it represents occlusion by marking the region that was occluded as 0.)
    src_pixel_coords = cam2pixel(cam_coords, proj_cam_to_src_pixel[:,:,:3], proj_cam_to_src_pixel[:,:,-1:], padding_mode)  # [B,H,W,2]
    projected_feat = torch.nn.functional.grid_sample(feat, src_pixel_coords, padding_mode=padding_mode)

    return projected_feat, src_pixel_coords

To achieve this, I used invers_warp.py to return src_pixel_coords (i.e. target coordinates), which is passed as an argument to the torch.nn.functional.grid_sample() function.
From this, src_pixel_coords value, I can produce the binary map I desire by following numpy code:
warped_img, src_pixel_coords = inverse_warp( ... )

img_height = 480
img_width = 640

coords = (src_pixel_coords + 1) / 2       #0-1 normalize
x_coords = coords[0,:,:,0] * img_width  #batch_size = 1
y_coords = coords[0,:,:,1] * img_height

x_coords = x_coords.floor().clamp(0,img_width-1).cpu().numpy()
y_coords = y_coords.floor().clamp(0,img_height-1).cpu().numpy()

mask = torch.zeros(img_height, img_width)
for y in range(img_height):
    for x in range(img_width):
        y_ = np.clip(2*y-int(y_coords[y,x]), 0, img_height-1)
        x_ = np.clip(2*x-int(x_coords[y,x]), 0, img_width-1)
        mask[y_, x_] = 1

And the output:

It works properly and produce binary map well. However, there are several problems with this code.

It is implemented in numpy, therefore it can’t be computed on GPU.
Of course, it doesn’t work when batch size> 1.
Bad time complexity.

I would like to implement this numpy code as functions for a pytorch tensor and solve the above three problems. However, I am not sure how to solve this problem. Can you help?