Comments (2)
We use sparse LiDAR points to generate dense depth images. Some details are mentioned at #7. The data format of the depth_u16.png is: depth_u16 = 1000 * real_depth (in meters).
If you only have color images, one way to obtain dense depth images is to adopt existing monocular depth estimation networks. However, the performance of freespace detection may degrade since the depth images generated by monocular depth estimation networks are not as accurate as those generated from sparse LiDAR points.
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Hello Wang, I try to generate dense depth images from lidar data too. But the generated depth image result seems different from your provided depth_u16. It also influences the inference performance. Can you provide your dense depth images generation code for us? Thanks a lot! @hlwang1124 My email: [email protected]
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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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