Some confusion about the paper and code

[Hui-Yao]

Some confusion about the paper and code

[zhou13]

1. It is not used in this project but in https://github.com/zhou13/shapeunity. Reserved for C-Junction and T-Junction.
2. It is defined at

   lcnn/lcnn/models/line_vectorizer.py

   Line 231 in e07e48b

   feat = torch.cat(

   I don't know how to explain it further.
3. It is the junctions wholse score > 0.03.
4. This is similar to 1. Reserved for C-Junction and T-Junction.
5. Subpixel offset compensation.

[Hui-Yao]

@zhou13 Thanks for you reply.
(1)Does the variable feat represent some imformation(such as Inclination degree) forcibly injected into the feature?
Or how should i understand the role of feat?

 feat = torch.cat(
 [
   xyu / 128 * M.use_cood,     # uv coordinate of endpoint 1 of the chosen lines, shape of (num_chosen_line, 2)
   xyv / 128 * M.use_cood,     # uv coordinate of endpoint 2 of the chosen lines, shape of (num_chosen_line, 2)
   u2v * M.use_slop,           # Inclination degree(tan)  , shape of (num_chosen_line, 2)
   (u[:, None] > K).float(),   # whether the index of chosen enpoint is greater than K, shape of (num_chosen_line, 1)
   (v[:, None] > K).float(),   # K is defined as K = min(int(N * 2 + 2), max_K) durning training
],  
   dim=1, )    # feat.shape = (num_chosen_line, 8)

(2) Can you explain about the subpixel offset compensation briefly, or just send me a web link abot it? I cant search useful information about that concept on Baidu and Google.

Thanks again！

[zhou13]

1. Yes. There is a section in the appendix of the paper describing it.
2. 0.5 offset is there because of my definition of the coordinate of the image. You need to define whether the center of the pixel is at (0, 0) or (0.5, 0.5).

[Hui-Yao]

@zhou13 Thanks very much, and have a good day !