Comments (2)
Hey @cattaneod,
Thanks for using our code.
As mentioned in the readme, for the fast LiDAR scan preprocessing you could use our SuMa++ which exploits OpenGL shaders to fast rendering the range and normal images.
We use that for the online SLAM and loop closing, where the preprocessing of the scan takes around 10 ms.
For generating range and normal training data without using GPU and OpenGL, we also provide a fast C++ implementation here, which is faster than the python implementation.
I hope this helps.
from overlapnet.
Thank you @Chen-Xieyuanli,
I tried the C++ implementation, and it's much faster than the python version, it takes around 9ms for the normal computation, so similar to the time reported in the paper.
from overlapnet.
Related Issues (20)
- Questions about my test HOT 2
- Questions about overlapnet HOT 2
- Using overlapnet for a different dataset HOT 6
- Question about covariance ? HOT 2
- Question about computing overlap ground truth HOT 4
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- Some confusion about normalize_data.py HOT 3
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- Pytorch version training. HOT 3
- Questions when Generating train_set HOT 5
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- Problem in the first version HOT 2
- ground truth HOT 1
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from overlapnet.