Hi,

I've been working on a merge of your codebase with the official OpenPCDet from OpenMMLab for the supervised part. The purpose would be that OpenPCDet would support supervised training on ONCE directly.

I would like to make a PR of my merge on OpenPCDet when it's ready, but I will wait for your agreement on that. Also if you have specific requirements on License, citation or parts of the code I should not include, don't hesitate to tell me, and we can see with OpenMMLab what can be done.

Hi, @PointsCoder , thanks for sharing the source code, I have one question about the code after reading, for 3dioumatch, why you use cls_score as nms_score ? does't it give better result? as now many detector use iou score as nms score

@PointsCoder Thank you for the excellent ONCE dataset and baseline. Now the 'ICCV 2021 Workshop SSLAD Track 2 - 3D Object Detection' competition is over. When will the permanent phase start, and when will the results of the test set be available? Thanks a lot.

Hi, after reading the semi-supervised config file, I find for all semi-supervised method, you didn't use gt-aug augmentation method for labeled data, did you already do experiment and find gt-aug didn't give improvement in semi-supervised learning, or just for convenient。

Hi,

First of all, thank you for the great work of putting different semi-supervised methods on lidar point clouds into a single repo.

I have a question on the Noisy Student training. From the Noisy Student config https://github.com/PointsCoder/ONCE_Benchmark/blob/master/tools/cfgs/once_models/semi_learning_models/noisy_student_second_large.yaml, it does not seem to add dropout DP_RATIO into the model. But the Noisy Student paper suggests to add it. Not sure if I am missing something?

Also, the Noisy Student training seems to be for only 1-cycle, instead of 3-cycles as originally done in the paper. Could you please let me know if the multiple cycle experiment lowered the performance compared to only 1-cycle?

On comparing Noisy Student to Pseudo Labels config, it appears the only difference between the 2 being random augmentations of random_world_flip and random_world_rotation are not applied to Student model in Pseudo Labels. Could you please confirm if that's the only difference between these?

Looking forward to your reply.

Thank You !! Anuj

How can I continue to obtain the results of the ONCE test split data after the 'ICCV 2021 Workshop SSLAD Track 2 - 3D Object Detection' competition is over? Thanks！

I have downloaded and unpacked the whole ONCE Dataset and have cloned this repo, but I still can't find the ImageSets folder and any description or information about it. How can I get the ImageSets folder? Thanks a lot!

In tools/cfgs/once_models/uda_models/waymo_to_once/secondiou_waymo_origin.yaml, the SHIFT_COOR parameter is set to [0,0,0] and [0,0,1.6] is noted. However, the origin of corrdinates in Waymo point cloud is on the ground while that in ONCE point cloud is the LiDAR sensor. Thus, there exists origin shift between two datasets and why you use [0,0,0]. Thanks!

Hi, I was reading this project recently and I found that there is something different from the original implementation of the 3D IoU Match. In the paper of 3D IoU Match, the EMA was applied for updating the teacher model which was not used in your project. I also realized that in your paper ONCE, the description of the 3D IoU Match also missed the EMA part in figure3. I was wondering whether this is the reason for the less competitive performance of the 3D IoU Match as mentioned in the paper ONCE. Anyway, thanks for this awesome work.

Hello~I'm wondering why do you encapsulate the DistributedDataParallel model as follows? https://github.com/PointsCoder/ONCE_Benchmark/blob/3bc1f3ab2f5715ea9e54ee10250a829876abf563/tools/semi_train.py#L57-L74

Hi

The coordinate of original ONCE lidar point clouds is x pointing left, y pointing backwards. However, I can't find the code to explicitly transform the once lidar coordinate to the unified normative coordinate as the openpcdet documentation states.

The getitem method of once https://github.com/PointsCoder/ONCE_Benchmark/blob/aebe9dc8ceefb198719aa6938a97115f55add794/pcdet/datasets/once/once_dataset.py#L126-L153

The get_lidar method of once https://github.com/PointsCoder/ONCE_Benchmark/blob/aebe9dc8ceefb198719aa6938a97115f55add794/pcdet/datasets/once/once_dataset.py#L73-L74

The load_point_cloud method of once

def load_point_cloud(self, seq_id, frame_id):
    bin_path = osp.join(self.data_root, seq_id, 'lidar_roof', '{}.bin'.format(frame_id))
    points = np.fromfile(bin_path, dtype=np.float32).reshape(-1, 4)
    return points

mean teacher result ,which test on validation set and train on small unlabel set. |AP@50 |overall |0-30m |30-50m |50m-inf | |Vehicle |73.31 |84.82 |68.15 |52.92 | |Pedestrian |31.88 |36.56 |27.27 |18.93 | |Cyclist |61.41 |73.01 |54.92 |37.50 | |mAP |55.53 |64.80 |50.12 |36.45 |

the ap is lower than the paper, especially the distance is 0-30m

Hi, what a great dataset!

Could you plz tell me what is the relative distance of the LIDAR coordinate system's origin to the ground? I have searched the official website and paper but didn't find this information. I look forward to your reply. 请问是否可以告知激光雷达坐标系原点距离地面的相对高度为多少？查找了官网和paper都没有找到这个信息。 期待您的回复。

#27

Hello,

Thank you so much for your work and for making this data freely accessible. It is very impressive and awesome. But I have a question, do you have the calibration information of the lidar ? ex. the calibration of lidar to road information, the calibration of lidar to earth information.

best regards, L

Hi All, Thank you for this great repo! To make this discussion easier lets take for example file 000080.json which lies under <PATH TO DATA>/data/once/data/000080. When loading this file it contains 3 keys and under frames we can find the annotations. As far as I can see every 2nd entry does not contain annos field, does it suppose to be like that? If not how should I handle it?

Cheers, A

Hi All, Thank you for your excellent work! I have 2 questions:

1. You request to organize the dataset as follow:

ONCE_Benchmark
├── data
│   ├── once
│   │   │── ImageSets
|   |   |   ├──train.txt
|   |   |   ├──val.txt
|   |   |   ├──test.txt
|   |   |   ├──raw_small.txt (100k unlabeled)
|   |   |   ├──raw_medium.txt (500k unlabeled)
|   |   |   ├──raw_large.txt (1M unlabeled)
│   │   │── data
│   │   │   ├──000000
|   |   |   |   |──000000.json (infos)
|   |   |   |   |──lidar_roof (point clouds)
|   |   |   |   |   |──frame_timestamp_1.bin
|   |   |   |   |  ...
|   |   |   |   |──cam0[1-9] (images)
|   |   |   |   |   |──frame_timestamp_1.jpg
|   |   |   |   |  ...
|   |   |   |  ...
├── pcdet
├── tools

But on the dataset website it does not look like that. In addition, I extracted the train annotation tar file and did not find any .txt file. Can you please check it out or update the "getting started" file + Dataset object?

2. Did you perform a benchmark on multimodal model? if so, which model did you use?

Cheers, A