Comments (5)
Sorry that I am not familiar with this task. Considering that our network is designed for taking two different modalities of data as input, I think you can try our approach if your task also has such demand.
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It's dividing the document into columns,
The column effect is as follows: red on the left represents label 1, and yellow on the right represents label 2:
from sne-roadseg.
According to your description, I think it is more related to an object detection problem than a semantic segmentation problem.
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from sne-roadseg.
I think you can try existing instance segmentation approaches such as Mask-RCNN, because it is more appropriate to divide different columns with different ids than different labels.
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Related Issues (20)
- Testing without depth image input HOT 1
- About palette.txt HOT 1
- error while training
- calib
- KITTI Sequence 00-10 HOT 1
- Save images
- Campus Dataset
- Question about result
- No such file 'latest_net_RoadSeg.pth' HOT 2
- calibration file of R2D dataset HOT 3
- usage of depth image in R2D dataset
- how to convert depth image in R2D HOT 1
- Questions on SNE-RoadSeg+
- Questions about sne
- kitti dataset without validation HOT 1
- R2D issue
- transform depth map to disparity map
- SNE-RoadSeg+ issue
- diffKernelArray HOT 1
- Inference on own data HOT 2
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