leihui6 / acp-3detection Goto Github PK

Hello, first of all thank you for you work,
I wanted to ask why did you chose to move the scene of 7m exactly ( it worked better for me as well but I'm wondering why)
I was in a comment that, for kitti, the scene is a bit far from the center (velodyne), but i wanted to know the exact reason why 7m or was it just a choice ?

Thanks in advance

Please help me, is it possible to train the model with custom dataset , how can I train and inference the model with custom dataset , if possible please help me , Thank you so much in advance.

Is training script provided? I want to add my own data to the training

Hi @Gltina
can you explain more where did you get value 0.305m) and 7m , 1.75m is height of velodyne to ground in kitti

Height info:
kitti_camera : 1.73 m
PMD_camera in this project: 0.796 m
1.73 - 0.305 = 1.425

negative direction
x + 7m
transform_matrix = np.mat(np.array([
[0, 0, 1, 7],
[1, 0, 0, 0],
[0, 1, 0, -1.425],
[0, 0, 0, 1]]))

Hi, I am not familiar with this piece. I would like to reproduce the training and validation process based on your dataset, can you provide the corresponding commands or tutorials?

[0, 0, 1, 7],
[1, 0, 0, 0],
[0, 1, 0, -1.425],
[0, 0, 0, 1]]))
you are translating your points from 0.305----->1.73 then why you put -1.425 instead of +1.425 ?
Please help madam,thanks in advance

Hi, Gltina,
Thank you for proving this wonderful repository.
I tried to use Supervisely to annotate my lidar data and I got a simple json file. The labels in json are quite different from those in KITTI label. You provided a tool to convert json to KITTI label , however, I am wondering if you have done any other conversion before feeding json( as below) to converter_mylabel2KITTIlabel.py since there were errors putting json directly into this function. If so, how did you do that? Thanks.

Hai,
I had used your pretrained model given checkpoint_epoch_250.pth and your data(Fine) to check the output but when I use the demo.py I got the output as [below. I didn't get the output as yours given in the .gif file

ie No 3d Box

Hi Gltina, thanks for your amazing work.

I used the custom dataset method that u mentioned in here. And I've get some not bad result which is in KITTI format. So I want to evaluate the result, but the kitti evaluation is not support for my custom result, so I read your paper, and want to use your method to get overlap volumes between ground truth and final_result.

I tried to use the evaluation.py code that u have made, but I can't get the right result, which I think should output the overlap volumes.
I put the trained and standard label fold as u mentioned:

and I get the result :

This is the command that I input:
evaluation.py trained_label

and the standard label example:
tractor 0.00 0 0.00 0 0 50 50 2.74 2.12 4.33 4.28 1.28 5.2 0.0

the trained label example:
tractor -1 -1 -3.8309 0.0000 0.0000 0.0000 0.0000 2.5715 2.2279 4.3080 4.1810 1.2036 5.1874 -3.1535 0.9790

I would appreciate it if you can help me figure out where is my problem, many thanks to u in advance.

Hi,gltina
Specific parameters in this project,Socket and Plug,1.POINT_CLOUD_RANGE: [ ] 2.anchor_sizes[ ],
In OpenPCDet, the KITTI dataset looks like this POINT_CLOUD_RANGE: [0, -40, -3, 70.4, 40, 1]，Car 'anchor_sizes': [[3.9, 1.6, 1.56]],
I had used your pretrained model given checkpoint_epoch_250.pth 、finedata，labelfile
Use converter_ Pc2KITTIPC converts the data into the same coordinate system as the KITTI dataset，
Meanwhile modified some configurations according to the instructions in OpenPCDet Custom Dataset.
In label, the object size is affected by the label, and the size is different
I hope you can provide the specific parameters of the above two configurations. Thank you!