Thanks for your great work!

I use the released code to retrain the network, while the results are strange. I obtain: [email protected] [29.223935810025136, 21.975801299792906, 19.0762136467218] [email protected] [17.863160821111062, 12.961739635817185, 10.802839248636912]

where the AP_BEV is OK, but the AP_3D is considerably low. I tried three times and get similar results.

作者你好，在数据增强的水平翻转操作中，若图像进行了翻转，那么相机的相关标定信息会发生变化。在您的代码中体现在kitti.py中的calib.flip(img_size)这一操作，但是我不是很理解函数中为什么要构造cos_matrix这个矩阵以及用奇异值分解来求解相关系数，因此想向您请教一下该函数的相关数学原理出处，期待回复，非常感谢!!!

HI, @SuperMHP Thanks for your codes! When evaluate the mAP|40, I use the cpp script you provided to get results. As is shown in the following figure, I wonder what is the difference between car_detection_ground and car_detection?

Thanks for your great work! I am now training the code under pytorch 1.10 and cuda 11.0, because I don't have a proper GPU that satisfies the environment in README. However, I got a much lower result in AP40 moderate: 13.69, compared to the given ckpt 16.23. Do you have some ideas about why the performance deteriorate sharply under different environments? Thanks very much

bias_depth = 1. / (depth_net_out[:,0:1].sigmoid() + 1e-6) - 1. ∈(0, +inf) the bias_depth need be negative number if the geo_depth was bigger than real_depth.

@SuperMHP Hi, thanks to your work. I want to know if map=16.46 only uses car category during training

I trained my GUPNet by ['Car','Pedestrian','Cyclist']. Unfortunately, I tried to train three times and could only reach up to 15.5! So, I doubt how you can train out model with mAP=16.46. (Is the kitti training error of more than 1 point too large?) Whising for your reply.

Hello, you have done a great job! I am some confused about the lib.datasets.kitti_utils.Calibration.flip function, which is used in your kitti dataloader. I am not sure about its function and result. when I try to use the flipped calib to back project some points from fliped image to the camera coordinate, I got very wrong result. I wonder the lib.datasets.kitti_utils.Calibration class is written by yourself or referenced from some orther codebase, Thank you very much!!! Thanks!

https://github.com/SuperMHP/GUPNet/blob/f4e26601ba5551065c36ab7ecf17f6915015d311/code/lib/models/gupnet.py#L165 according to h3d should be equal to

h3d = self.mean_size[cls_ids[mask].long()] + size3d_offset * h3d_std

thx

Dear author,

Thanks for your wonderful work. I am following your repo to build a 3D detection framework. Do you mind telling me the difference between the results of original paper and released ckpt (especially on the test set) . And what causes the performance gap between them?

Hey! Great work!

I was trying the train_val.py in the evaluation mode using the model you provided. However. I see that all the detections are all 0. Any idea why this might be happening?

Thanks!

Hello , Thanks for your great work! And we hope to follow your work. We retrained your code on the KITTI train split (3dop), and evaluate it on the val set, get car's AP in val set: [22.698555, 15.741446, 13.477293], and it is close to your paper's report. Then we just use this checkpoint(trained on train split) to infer in KITTI's test set, and submit the result to KITTI benchmark, get the following result in test set:

The 3D AP is very low(Car's 3D AP: 14.93%, 10.15%, 8.22%). I wonder if it is normal considering the model is trained on train split(not in trainval).

And we want to reproduce your paper's report on KITTI test set, close to car‘s AP（22.26%, 15.02%, 13.12% as your released checkpoint or 20.11%, 14.20%, 11.77% in your original paper）. In addition to setting to the trainval set, what else do we need to do? And if I set to trainval set, how do I choose the best checkpoint in case that the val set is included in the trainval set? Thank you very much for your reply！