Comments (1)
Hi. Our approach is a data-fusion one that requires RGB images and the corresponding depth images as input. Many road datasets such as KITTI and Cityscape provide these kinds of data. If you are using your own dataset that does not contain depth images, I suggest you either 1) use other single-modal networks (e.g., DeepLabv3+) or 2) first use monocular depth estimation approaches to generate the depth images and then use our data-fusion network.
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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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