weikaitan / toronto-3d Goto Github PK

Hi,

Thanks for your nice work. However, the evaluation metrics of OA may be confusing. OA should be the ratio of the number of correctly predicted points to the total number of points, right?

Could you please specify the license of the dataset? Thanks

您好，请问目前公开的准确率的模型是否能提供预训练模型，我自己测试的KPFconv以及pointnet++并不能达到上述准确度，目前测试的准确度和链接论文所获取的结果相似。链接

Hi, @WeikaiTan ,

Thanks for releasing such a promising dataset. Besides the dataset, could you also release the evalutation tools for segmentation task? It's essential for building a complete benchmark .

THX!

Hi, I noticed that you reported the RandLA-Net performance on the Toronto-3D dataset, could you please release the pre-trained model and evaluation code for RandLA-Net?

Hi @WeikaiTan

Thank you for this great dataset! I wonder if there are any examples for loading and using the dataset?

Best,
Yongcheng

Hi, thank you for providing this excellent dataset. I noticed that there are several errors in the labeling. I have attached an example in which the floor above the cars is classified in the same floor class. Does this affect the results or not very much?

Hi @WeikaiTan, thanks for your nice work. Could you please share the car labels data you used in the study with me? Thanks you so much.

Hi @WeikaiTan ,thanks for your nice work. I found that the legend of class labels in Figure 1 exits some problems in your papar. The Pole class is missing and that results the Car and Fence get a wrong color-to-label.

Hi @WeikaiTan

Could you provide a dataloader.py for the Toronto3D dataset? I want to see the specific data format to suit for the semantic segmentation baseline. I'm testing my algorithm on Toronto3D dataset, but can't load the data successfully. Or, could you please provide the code of RandLA-Net on Toronto3D as you shown in the table?

In addition, have you ever run the Open3D-ML framework for Toronto3D dataset? I have run RandLANet on Toronto3D dataset, but I only get half the mIoU with TF version as reported. The pytorch version always show can't pickle bugs due to the torch.multiprocessing module.

Thanks a lot in advance.

Xiaobing Han

Hi, thanks for sharing the great work. I appreciate it. Can you please tell me which labeling tool you have used for preparing the segmentation annotations for this dataset? Thank you.

Hello, how do I visualize segmentation results?

Hi @WeikaiTan, thanks for your efforts in this area!
However, I just found there is a series of nan value in the label of the data, can you please check the label value of the dataset?

I just use np.unique(labels) and found the following results:

Many thanks!

Hi
I found some bug in L003.ply when I use plyfile to read L003.ply. much intensity of some points are NaN。 Codes are follows:

plydata = PlyData.read(filename)
pc = plydata['vertex'].data
pc_array = np.array([i.tolist() for i in pc ])

I would like to congratulate you for assembling this dataset. Like other semantic3d datasets that are available for analysis like S3DIS, Scannet etc.. in torch opints3d can we make this one similar to that? Please let mek now what you think. for your reference here is a link https://torch-points3d.readthedocs.io/en/latest/src/api/datasets.html

作者您好，请问能否提供一下toronto-3d数据集训练randlanet的预训练模型吗 我的邮箱[email protected]

Hello @WeikaiTan,
Is it possible to share with as the pre-trained model on RandLA-Net? I'm trying to train the model on data but I get weak mIoU (28%) using your code. I kept the same parameters.