This model file checkpoints/hv_pointpillars_secfpn_sbn_2x16_2x_waymo-3d-car-9fa20624.pth in the docs cannot be found in mmdet3d official repo (they only have the interval-5 pretrained models). Are the proposals extracted with interval-1 models: 3d-car and 3d-3class? If I want to reproduce your results, do I need to first train with these two configs? Thanks.

hi~ Zhichao Li /Feng Wang/ Naiyan Wang~

I am very interested in your work LIDAR RCNN, but when I use the LIDAR RCNN pretrained model you gave me checkpoint_lidar_rcnn_59.pth.tar(MD5:6416c502af3cb73f0c39dd0cabdee2cb, I found that the weights of the pretrained model are 9 dimensions, but your input data is 12 dimensions.

Can you provide me a pretrained model whose dimensions are correctly matched.

I found that in one of your commits, the dimension was increased from 9 to 12 dimensions, but the latest pre-trained model is still 9 dimensions

When I transfered it to the CenterpointNet and nuscenes datasets, Then evaluated on nuscense, it didn’t seem to work. I don’t know what went wrong, Looking forward to your suggestions and comments.

Hi, I am able to do all setup as per instructions given in README In the evaluation step,

python -m torch.distributed.launch --nproc_per_node=4 tools/test.py --cfg config/lidar_rcnn.yaml --checkpoint outputs/lidar_rcnn/checkpoint_lidar_rcnn_59.pth.tar
python tools/create_results.py --cfg config/lidar_rcnn.yaml

I am facing the following questions while running the evaluation.

1. How to change the command to run a single GPU, nproc_per_node needs to be 1.
2. What should be MODEL.Frame number for checkpoint_lidar_rcnn_59.pth.tar? Since I am trying to understand the evaluation, kindly help me on this to fix.

Thanks for your awesome works. When I use Lidar-RCNN on my own dataset, the refine score is useless, Most objects are classified as backgrounds. In addition, the average refined center error is only reduced by 1 cm. I don't know Is this normal?

Hello, just like the title saying, I wonder what are the specific processes for WOD, which means if I want to use LIDAR R-CNN on my own dataset, I have to do it differently. I already change the data_processor and everything I can think of in the loader and creat_results that are respect to waymo dataset, then I use the refined results to perform evaluation on my own dataset. However, I got NAN on rotation error, and the MAP is pretty low.

Therefore, I'm confused about some subtle processes that are performed just for waymo not for other datasets. For example, compute heading residual is necessary for using LiDAR R-CNN? Did you guys use rotation in some sublte ways? （In my dataset, the rotation is according to y axis, while in your code, it's x axis, but the way of computing rotZ is the same, I already changed it.）

This bug has been driving me crazy, that's why my issue description above is a bit messy, forgive me please. I would be grateful if you could provide me some hints. Thank you a lot. Save this almost desperate kid, please.🥺

Hello, sorry I come back with another question...... Recently, I've been working on using LiDAR R-CNN to refine the results of the CenterPoint-PP model with my own dataset. During data processing for my own dataset, I notice that the results of my CenterPoint-PP model has more bboxes detected than the ground truth ones (false detection case). When performing get_matching_by_iou function in LiDAR R-CNN, the obtained matching_gt_bbox has the same number of bboxes as the model predictions instead of the groundtruth data. I'm a bit confused about this process. Now that we are trying to do refinement, shouldn't we remove the falsely detected bboxes in the results and keep to the groundtruth? If so, why the matching bboxes is according to the predictions instead of groundtruth?

Maybe I have some misunderstandings here, it would be a great helper if you could give me some hints. Thanks in advance.

Hi, I am very interested in your paper, and I am reproducing it. The pretrained model of pointpillar provided in mmdetection3d does not reach the performance shown in the Table 2 below, so could you please provide the pretrained model of pointpillar in Table 2? Thank you very much!

Hi~Sorry to bother you again！ Is that right? The prediction frames of all frames are extracted at one time and then disrupted globally, which means that when lidar RCNN trains a batch, it contains different boxes of different frames. When the batchsize is 256, the extreme case may contain up to 256 frames, and each frame takes a box. Below is my idea！ If I train two frames at a time, extract proposals through the frozen one-stage network, and then use lidarcnn for end-to-end training, is it ok？Do u have an idea about how to design the ROI sampler ratio?

Hi developers of LiDAR R-CNN,

Congrats on the acceptance of the paper!

LiDAR R-CNN achieves new state-of-the-art results through simple yet effective improvement, which is very insightful to the community. We also found that the baseline is based on the implementations in MMDetection3D.

Therefore, I am coming to ask, as we believe LiDAR R-CNN might have a great impact on the community, would you like to also contribute an implementation of LiDAR R-CNN to MMDetection3D? If so, maybe we could have a more detailed discussion about that? MMDetection3D welcomes any kind of contribution. Please feel free to ask if there is anything from the MMDet3D team that could help.

On behalf of the MMDet3D Development Team

BR,

Wenwei

hi~ Zhichao Li /Feng Wang/ Naiyan Wang~ 我对你们的工作LIDAR RCNN非常感兴趣，但是我在使用您给我的LIDAR RCNN预训练模型checkpoint_lidar_rcnn_59.pth.tar(MD5:6416c502af3cb73f0c39dd0cabdee2cb 时，发现预训练模型的权重是9维，但是你们的输入数据是12维12维 您可以提供给我维度可以正确匹配的预训练模型吗