PRBonn / semantic_suma

SuMa++: Efficient LiDAR-based Semantic SLAM (Chen et al IROS 2019)
MIT License
923 stars 205 forks source link

different result on suma++ and rangenet++ #52

Closed xdtzzz closed 8 months ago

xdtzzz commented 2 years ago

Thanks for your code! When I run rangenet++ on my trained dataset, the result is almost correct, but when I run suma++ through the model.trt generated by rangenet++, the result has many wrong label, like the car , the top of the wall, and the people . In my opinion, if the result of rangenet++ is correct, then suma++ should be correct, too. What's the problem? This is the result of rangenet++. 2021-12-23 13-22-55 的屏幕截图 This is the result of suma++. 2021-12-23 13-24-46 的屏幕截图

Chen-Xieyuanli commented 2 years ago

Hey @xdtzzz, thanks for using our code.

The noisy map results may be caused by the wrong prediction and inaccurate poes estimation.

For example, the points of a car were predicted to be both car (blue) and house (yellow) in the segmentation results. When aggregating all the predictions together, it will become more obvious.

xdtzzz commented 2 years ago

谢谢您的回复! 您的意思是,每个类别都必须要分割的十分精确才可以使suma+达到良好的效果吗? 在rangenet运行,如果一个物体里大部分点云被成功的预测了,少量点云是错误的,就会导致suma+里面很多点云都是错误的情况吗

发自我的iPhone

------------------ Original ------------------ From: Xieyuanli Chen @.> Date: Thu,Dec 30,2021 3:54 PM To: PRBonn/semantic_suma @.> Cc: xdtzzz @.>, Mention @.> Subject: Re: [PRBonn/semantic_suma] different result on suma++ and rangenet++(Issue #52)

Chen-Xieyuanli commented 2 years ago

Q: Do you mean that SuMa++ needs very good semantic segmentation results to work well? A: Yes, SuMa++ itself can not improve the semantic segmentation results, but only use the semantics to improve the SLAM results. Wrong predictions will lead to a wrong semantic map. We originally use RangeNet++ which achieved an IOU of 52.2 and worked well. The segmentation results seem not as good as the original RangeNet++. What's your segmentation performance in terms of IOU? The current SOTA one is of IOU 70, and you may use the SOTA semantics to get a better semantic map.

是的,SuMa++对semantic segmentation本身是没有提升的,只是利用语义信息来提高SLAM的精度,如果语义信息不准,语义地图也是会不准的。 原文用的rangenet++的IOU是52.2是可以工作的,当前的语义分割结果似乎比原文差,请问现在语义分割的IOU精度是多少? 目前性能最好的语义分割算法性能是70IOU,可以尝试使用更好的语义分割算法。

xdtzzz commented 2 years ago

我现在语义分割的iou精度挺高的,因为标注的类别比较少,平均达到了0.8以上

xdtzzz commented 2 years ago

Results on rangenet++, most of them are correct. But on suma++, there are many wrong semantic points. For example, the ground points build on the top of the wall on suma++, but on rangenet++, the wall doesn't have ground points. rangenet++: 0b16510ed42860c0783357589a2c7cc suma++: 958db1246aa6e3689bfd18e7d032964

xdtzzz commented 2 years ago

where is "reopen" button.... # @Chen-Xieyuanli