martinhahner / lidar_fog_sim

LiDAR fog simulation

lidar_fog_sim's Issues

the wrong results of fog simulation on waymo and Kitti

Thank you for your excellent work.

In fact, it is similar to issue "the wrong result of the fog\u simulation.py using the Kitti data. \7". I directly run the "fog_simulation.py" file on Kitti and waymo datasets. The results are similar to issue 7. It seems that almost all points are regarded as fog noise.

I want to know whether it needs to be modified due to the intensity of the data or whether it is impossible to directly run fog_ simulation. Instead, I need to run pointcloud_viewer.py (doesn't sound reasonable)

Looking forward to your reply

Question about evaluation on dense fog weather in STF Dataset

Hi Martin,

Thank you for providing this elegant and powerful repo and I really appreciate your brilliant work!

I'm trying to reproduce the training pipeline on Seeing Through Fog Dataset. In the experimental part of the paper, there are clear weather baseline and "strongest ∩ last filter" baseline. I want to know whether clear weather baseline is evaluated on strongest return or last return of dense fog test split. I don't seem to find a description in the paper or in the supplementary materials, so sorry if I missed it.

Thank you in advance!

The wrong result of the fog_simulation.py using the kitti data.

Thank you for your excellent contribution of this work！There is a problem when I run the fog_simulation.py to generate the fog by using the kitti dataset.


The 1st picture is the original kitti data.
The 2nd picture is generated by the fog_simulation.py.
I wonder how can i solve the problem,thank you.

when l use pointcloud_viewer.py l get the fog simulate on kitti like this

Visualizing output of demo.py of OpenPCDet using a Headless Server

Hello
First of all, thank you for these codes, they've helped me a lot.
I am a newbie to 3D object detection, I saw the demo.py code that you wrote in OpenPCDet, I have used it and got as an output the data_dict_idx.pkl and pred_dicts_idx.pkl .
Now I am trying to visualize these outputs using your pointcloud_viewer but when i choose the custom directory where i put the demo.py output, nothing shows up, I was wondering if you could help me

Does the simulation results match real world data?

Thanks for your excellent work. I am hoping to use the simulation to generate some synthetic foggy data, e.g. on KITTI. Hence, I am wondering how much does the simulated data match with real-world foggy data? But I found only some minor qualitive discussion is represented in the paper, no quantitive discussion.

How do I add fog simulation to my dataset

Can I only use the dataset in the introduction of the code, what if I want to add this fog simulation to my dataset?

some questions for the 3D average precision (AP) results on the STF

Hello, I have read your paper of "Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather," and I have some questions about it. Specifically, I used the STF dataset and divided the clear data into training, testing, and validation sets in a 7:2:1 ratio. I trained the PointPillar and PV-RCNN++ models on the clear training dataset and tested them on the clear validation set based on openpcdet. However, the results are not satisfactory, and I am unable to achieve the impressive results mentioned in your article. This issue has been bothering me for a long time. I apologize for the inconvenience, and I seek your assistance. Thank you.

some results as fellows:
Pedestrian [email protected], 0.50, 0.50:
bbox AP:15.5895, 15.2841, 15.5391
bev AP:11.0431, 11.1163, 11.3700
3d AP:10.4871, 10.5488, 10.2467
aos AP:11.37, 11.10, 11.26
Pedestrian [email protected], 0.50, 0.50:
bbox AP:9.3038, 9.4138, 9.3635
bev AP:3.7221, 3.5240, 3.7916
3d AP:2.8358, 2.6212, 2.5333
aos AP:5.32, 5.35, 5.32
Pedestrian [email protected], 0.25, 0.25:
bbox AP:15.5895, 15.2841, 15.5391
bev AP:15.1044, 15.0117, 15.5118
3d AP:14.8881, 14.7912, 15.3198
aos AP:11.37, 11.10, 11.26
Pedestrian [email protected], 0.25, 0.25:
bbox AP:9.3038, 9.4138, 9.3635
bev AP:8.7162, 8.9601, 9.1860
3d AP:8.4221, 8.4356, 8.8460
aos AP:5.32, 5.35, 5.32
PassengerCar [email protected], 0.70, 0.70:
bbox AP:0.0000, 0.0000, 0.0000
bev AP:0.0000, 0.0000, 0.0000
3d AP:0.0000, 0.0000, 0.0000
aos AP:0.00, 0.00, 0.00
PassengerCar [email protected], 0.70, 0.70:
bbox AP:0.0000, 0.0000, 0.0000
bev AP:0.0000, 0.0000, 0.0000
3d AP:0.0000, 0.0000, 0.0000
aos AP:0.00, 0.00, 0.00
PassengerCar [email protected], 0.50, 0.50:
bbox AP:0.0000, 0.0000, 0.0000
bev AP:0.0000, 0.0000, 0.0000
3d AP:0.0000, 0.0000, 0.0000
aos AP:0.00, 0.00, 0.00
PassengerCar [email protected], 0.50, 0.50:
bbox AP:0.0000, 0.0000, 0.0000
bev AP:0.0000, 0.0000, 0.0000
3d AP:0.0000, 0.0000, 0.0000
aos AP:0.00, 0.00, 0.00
RidableVehicle [email protected], 0.50, 0.50:
bbox AP:0.0000, 0.0000, 0.0000
bev AP:0.0000, 0.0000, 0.0000
3d AP:0.0000, 0.0000, 0.0000
aos AP:0.00, 0.00, 0.00
RidableVehicle [email protected], 0.50, 0.50:
bbox AP:0.0000, 0.0000, 0.0000
bev AP:0.0000, 0.0000, 0.0000
3d AP:0.0000, 0.0000, 0.0000
aos AP:0.00, 0.00, 0.00
RidableVehicle [email protected], 0.25, 0.25:
bbox AP:0.0000, 0.0000, 0.0000
bev AP:0.0000, 0.0000, 0.0000
3d AP:0.0000, 0.0000, 0.0000
aos AP:0.00, 0.00, 0.00
RidableVehicle [email protected], 0.25, 0.25:
bbox AP:0.0000, 0.0000, 0.0000
bev AP:0.0000, 0.0000, 0.0000
3d AP:0.0000, 0.0000, 0.0000
aos AP:0.00, 0.00, 0.00

Problem about loading openpcdet result.pkl

Hi! I came from OpenPCDet issues channel and found your amazing work!
But I have a problem about loading the result.pkl with kitti velodyne data. I can't visualize the predicted bbox in the point cloud.
Is there anything wrong with my file path with the pickle?
Thanks a lot in advance! The interface is really great.

I find that