Different semantic results of suma++

[TT22TY]

Hello,

Thanks for your work！ After I build the suma++, I just obtain the result like this, I use the pretrained model provided on the webpage, however, it seems that the semantic result is quite different from the picture you provided. I am wondering why it happened( Is it due to the pretrained model used?). I try to use the trained model which is trained from scratch by myself, it works well using the infer.py of RangeNet++, the result is also strange. Besides, the result of RangeLib are also different from the picture you provided.
Looking forward to your reply. :) Thanks!

[Chen-Xieyuanli]

Hey,

Thanks for using our code.

It looks like a problem of the semantic segmentation part, and the SLAM part works. You could visualize the semantic segmentation results by checking the box of "Show semantic map"

Several reasons may cause this problem.

1. Our rangenet_lib and the provided pretrained model can now only work well with KITTI LiDAR data. It may provide wrong predictions when you test with other LiDAR data.
2. The runtime tensorrt model will be generated depending on your hardware. It may not work with other versions of the graphic cards or drivers. Could you please provide more information about your hardware setups. We may also have a test later.

[Chen-Xieyuanli]

The problem should come from the rangenet_lib, since the semantic segmentation input of SuMa++ is not correct.

Have you already tried the example demo of rangenet_lib? The semantic segmentation result visualized in the "Show semantic map" should be the same as that of rangenet_lib.

You may test and make sure the rangnet_lib works first. If the example demo of rangenet_lib works well, SuMa++ should also work, and vice versa.

[Chen-Xieyuanli]

Hi @TT22TY,
Since the problem comes from rangenet_lib and you also opened a new issue there, I'm going to close this one.

[Chen-Xieyuanli]

Hi @TT22TY

The problem seems caused by the incompatibility of our code with TensorRT version 6.

For more details please find here: PRBonn/rangenet_lib#9.

[Chen-Xieyuanli]

Hi @TT22TY ,

I've listed the tested setups in PRBonn/rangenet_lib#9.

To keep this issue in one track, I am going to close this one again.