Open SiYLin opened 9 months ago
Thank you for your response. I appreciate your diligence in this matter. Upon my initial interpretation, I may have made a mistake.
I conducted tests on the KITTI dataset and the results aligned well with the data provided in your paper. However, when I applied the same tests to the Nuscenes dataset, I observed a significant drop in the XY position (5m), which fell to 48% recall under a maximum initial error of 32m.
I have verified the roll pitch that we used to rectify the camera (similar to the process what you used in KITTI dataset, pixel to camera, camera to vehicle, and vehicle to world). For my training, I only use the front camera data.
This is likely too small. If the training and validation data are in disjoint areas, you should see a clear overfitting.
Thx for your replying! It does overfit the training dataset. The recall half meters is 100% in training dataset....May I know how much data in general to train this model?
The more the better. Try initializing your model with the pre-trained Mapillary model that we provide.
Hi, Thx for the great work. I recently have tried to apply your work on various autonomous driving dataset and find out the performance is way lower compare to the information you gave in paper such as KITTI, under same max init error. E.g. The position XY 5m recall is only around 40% under 32 init error.
Hello, SiYlin, I've also been trying to migrate Orienternet to NuScenes recently. I wanted to ask you how you're getting GPS information. As far as I know, NuScenes only provides ground truth pose and doesn't include raw GPS information with noise. Thank you very much!
Hi, Thx for the great work. I recently have tried to apply your work on various autonomous driving dataset and find out the performance is way lower compare to the information you gave in paper such as KITTI, under same max init error. E.g. The position XY 5m recall is only around 40% under 32 init error.
Hello, SiYlin, I've also been trying to migrate Orienternet to NuScenes recently. I wanted to ask you how you're getting GPS information. As far as I know, NuScenes only provides ground truth pose and doesn't include raw GPS information with noise. Thank you very much!
Hi : You can try to add some random noise on the ground truth pose to fake the scenario in which you have noise GPS information.
Hi, Thx for the great work. I recently have tried to apply your work on various autonomous driving dataset and find out the performance is way lower compare to the information you gave in paper such as KITTI, under same max init error. E.g. The position XY 5m recall is only around 40% under 32 init error.
Hello, SiYlin, I've also been trying to migrate Orienternet to NuScenes recently. I wanted to ask you how you're getting GPS information. As far as I know, NuScenes only provides ground truth pose and doesn't include raw GPS information with noise. Thank you very much!
Hi : You can try to add some random noise on the ground truth pose to fake the scenario in which you have noise GPS information.
Got it, that's exactly what I'm doing now🤣, thanks.
Hi, Thx for the great work. I recently have tried to apply your work on various autonomous driving dataset and find out the performance is way lower compare to the information you gave in paper such as KITTI, under same max init error. E.g. The position XY 5m recall is only around 40% under 32 init error.