What is the meaning of the variable offset [line306] in the segmentation model？ And if I only have data in the form of [B, 3, N] can I even get the offset variable? Also, if I can't get the offset easily, how can I extract the point cloud features in the data of [B, 3, N]?

Really appreciate it if you can reply!

Hello,I tried to reproduce the experimental results in your paper,My results are similar to those in your paper in Area5, but I want to try the experimental results in 6-fold. if you can provide the code,I would be grateful！ thx

hello, I try to train the model on two gpus, but an error occurred. The error occurred when load the S3DIS dataset. The error shows that "ValueError: batch_size should be a positive integer value, but got batch_size=0", how can I solve this problem? Thanks for your help.

When I want to configure the environment, I find I can't find the folder ‘lib/pointops’, where can I download the relevant file and configure it? ☆⌒(*＾-゜)v THX!!

sorry, I just saw the s3dis area-5 result. how can i train this code for S3DIS 6-fold?

Another question： I see this code to feed too much point clouds into the network. If the input point cloud data will be reduced, speeding up training. Will this cause a drop in accuracy?

Dear authors, Thank you so much for your awesome work.

When I follow your source code to compile Pointops in Classification and then compile the other Pointops in Segmentation, the Pointops imported will come from Cls.'s.

This problem was found by

File './RepSurf/segmentation/modules/pointops/functions/pointops.py', line 126, in forward
    pointops_cuda.knnquery_cuda(m, nsample, xyz, new_xyz, offset, new_offset, idx, dist2)
TypeError: knnquery_cuda(): incompatible function arguments. The following argument types are supported:
    1. (arg0: int, arg1: int, arg2: int, arg3: int, arg4: at::Tensor, arg5: at::Tensor, arg6: at::Tensor, arg7: at::Tensor) -> None

Since knnquery_cuda() in Seg.'s Pointops support (arg0: int, arg1: int, arg2: at::Tensor, arg3: at::Tensor, arg4: at::Tensor, arg5: at::Tensor, arg6: at::Tensor, arg7: at::Tensor).

Then I found setup( name='pointops', ext_modules=[ CUDAExtension('pointops_cuda', in Cls's Pointops and setup( name='pointops_cuda', ext_modules=[ CUDAExtension('pointops_cuda', in Seg's Pointops. They have different python site-packages names but the same CUDAExtension name. This may cause upon problems during import pointops_cuda.

Because I am not familiar with C++, would you help me solve this problem? (The solution for now on is to delete the other site-packages while using one)

Thanks again for your great work.

Thanks for your excellent works !

I had a bit of a problem when reproducing the project. Could you explain the loss function (SmoothClsLoss) used in classification task? I didn't find an introduction to it in the paper.

https://github.com/hancyran/RepSurf/blob/ef873f8f36aa224e4b08edaab21330a828dac8f9/modules/repsurface_utils.py#L129

Thanks to the author for the excellent work, but I wonder why group_centriod = torch.zeros_like (sorted_group_xyz), why is the value of group_centriod all 0?

Hi, thank you for your great work. I have read your paper and I am curious about the backbone of the segmentation task. Do you use the same Decoder of PointNet++ or have any modifications on it?

Dear author, I'm trying to use my own dataset now. When I used the precalculated class weight of S3DIS, the results of training are normal, but the results of testing are very abnormal.The mIOU is only 6%. So what are the precalculated class weight and how to calculate that?

Could you help me? Thank you very much.

def get_class_weights(dataset_name):
    # pre-calculate the class weight
    if dataset_name == 'S3DIS_A1':
        num_per_class = [0.27362621, 0.3134626, 0.18798782, 1.38965602, 1.44210271, 0.86639497, 1.07227331,
                         1., 1.05912352, 1.92726327, 0.52329938, 2.04783419, 0.5104427]
    elif dataset_name == 'S3DIS_A2':
        num_per_class = [0.29036634, 0.34709631, 0.19514767, 1.20129272, 1.39663689, 0.87889087, 1.11586938,
                         1., 1.54599972, 1.87057415, 0.56458097, 1.87316536, 0.51576885]
    elif dataset_name == 'S3DIS_A3':
        num_per_class = [0.27578885, 0.32039725, 0.19055443, 1.14914046, 1.46885687, 0.85450877, 1.05414776,
                         1., 1.09680025, 2.09280004, 0.59355243, 1.95746691, 0.50429199]
    elif dataset_name == 'S3DIS_A4':
        num_per_class = [0.27667177, 0.32612854, 0.19886974, 1.18282174, 1.52145143, 0.8793782, 1.14202999,
                         1., 1.0857859, 1.89738584, 0.5964717, 1.95820557, 0.52113351]
    elif dataset_name == 'S3DIS_A5':
        num_per_class = [0.28459923, 0.32990557, 0.1999722, 1.20798185, 1.33784535, 1., 0.93323316, 1.0753585,
                         1.00199521, 1.53657772, 0.7987055, 1.82384844, 0.48565471]
    elif dataset_name == 'S3DIS_A6':
        num_per_class = [0.29442441, 0.37941846, 0.21360804, 0.9812721, 1.40968965, 0.88577139, 1.,
                         1.09387107, 1.53238009, 1.61365643, 1.15693894, 1.57821041, 0.47342451]
    elif dataset_name == 'ScanNet_train':
        num_per_class = [0.32051547, 0.1980627, 0.2621471, 0.74563083, 0.52141879, 0.65918949, 0.73560561, 1.03624985,
                         1.00063147, 0.90604468, 0.43435155, 3.91494446, 1.94558718, 1., 0.54871637, 2.13587716,
                         1.13931665, 2.06423695, 5.59103054, 1.08557339, 1.35027497]
    elif dataset_name == 'ScanNet_trainval':
        num_per_class = [0.32051547, 0.1980627, 0.2621471, 0.74563083, 0.52141879, 0.65918949, 0.73560561, 1.03624985,
                         1.00063147, 0.90604468, 0.43435155, 3.91494446, 1.94558718, 1., 0.54871637, 2.13587716,
                         1.13931665, 2.06423695, 5.59103054, 1.08557339, 1.35027497]
    else:
        raise Exception('No Prepared Class Weights of Dataset')
    return torch.FloatTensor(num_per_class)

thx for your great work! when I run the code of scripts/s3dis/train_repsurf_umb.sh,

The program stops running suddenly these are message:

[2022-11-18 14:58:19,302 INFO train.py line 326 16493] Epoch: [1/100][250/765] Batch 0.760 (1.679) Remain 35:34:19 Loss 0.9464 Accuracy 74.02 [2022-11-18 15:05:20,747 INFO train.py line 326 16493] Epoch: [1/100][500/765] Batch 0.590 (1.683) Remain 35:31:19 Loss 0.6425 Accuracy 81.22 [2022-11-18 15:11:53,662 INFO train.py line 326 16493] Epoch: [1/100][750/765] Batch 0.483 (1.646) Remain 34:37:36 Loss 0.4475 Accuracy 82.48 [2022-11-18 15:12:03,557 INFO train.py line 345 16493] Train result at epoch [1/100]: mIoU / mAcc / OA 51.48 / 71.95 / 75.43 [2022-11-18 15:19:06,043 INFO train.py line 326 16493] Epoch: [2/100][250/765] Batch 0.869 (1.690) Remain 35:26:02 Loss 0.4624 Accuracy 85.42 [2022-11-18 15:25:38,102 INFO train.py line 326 16493] Epoch: [2/100][500/765] Batch 0.814 (1.629) Remain 34:02:41 Loss 0.4129 Accuracy 83.37 [2022-11-18 15:32:28,147 INFO train.py line 326 16493] Epoch: [2/100][750/765] Batch 0.511 (1.633) Remain 34:00:33 Loss 0.2694 Accuracy 88.11 [2022-11-18 15:32:36,285 INFO train.py line 345 16493] Train result at epoch [2/100]: mIoU / mAcc / OA 69.06 / 87.74 / 85.77 [2022-11-18 15:39:29,681 INFO train.py line 326 16493] Epoch: [3/100][250/765] Batch 1.076 (1.654) Remain 34:19:14 Loss 0.2946 Accuracy 87.22 [2022-11-18 15:46:07,134 INFO train.py line 326 16493] Epoch: [3/100][500/765] Batch 0.494 (1.622) Remain 33:32:46 Loss 0.1570 Accuracy 93.33 [2022-11-18 15:52:44,975 INFO train.py line 326 16493] Epoch: [3/100][750/765] Batch 0.558 (1.612) Remain 33:13:30 Loss 0.2258 Accuracy 90.11 [2022-11-18 15:53:01,495 INFO train.py line 345 16493] Train result at epoch [3/100]: mIoU / mAcc / OA 76.53 / 91.84 / 89.52 [2022-11-18 16:00:07,540 INFO train.py line 326 16493] Epoch: [4/100][250/765] Batch 0.739 (1.704) Remain 35:00:31 Loss 0.1830 Accuracy 92.54 [2022-11-18 16:06:41,970 INFO train.py line 326 16493] Epoch: [4/100][500/765] Batch 0.582 (1.641) Remain 33:35:45 Loss 0.2703 Accuracy 90.42 [2022-11-18 16:13:25,494 INFO train.py line 326 16493] Epoch: [4/100][750/765] Batch 0.394 (1.632) Remain 33:17:57 Loss 0.1216 Accuracy 94.29 [2022-11-18 16:13:33,778 INFO train.py line 345 16493] Train result at epoch [4/100]: mIoU / mAcc / OA 82.32 / 94.37 / 92.11 [2022-11-18 16:20:25,855 INFO train.py line 326 16493] Epoch: [5/100][250/765] Batch 0.859 (1.648) Remain 33:30:38 Loss 0.2021 Accuracy 92.22 Traceback (most recent call last): File "tool/train.py", line 494, in <module> mp.spawn(main_worker, nprocs=args.ngpus_per_node, args=(args.ngpus_per_node, args)) File "/home/user1/miniconda3/envs/repsurf-seg/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 230, in spawn return start_processes(fn, args, nprocs, join, daemon, start_method='spawn') File "/home/user1/miniconda3/envs/repsurf-seg/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 188, in start_processes while not context.join(): File "/home/user1/miniconda3/envs/repsurf-seg/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 136, in join signal_name=name torch.multiprocessing.spawn.ProcessExitedException: process 1 terminated with signal SIGKILL

could you help me? thank U very much

Dear author

Why get the centroids by concatenating edges with edges.roll(-1, 2) ? # Algorithm 2 Pytorch-Style Pseudocode of Umbrella RepSurf pairs = concat([edges, edges.roll(-1, 2)], dim=-2) # [B,N,K,2,3] centroids = mean(pairs, dim=3) # [B,N,K,3]

Why not get centroids by mean(edges, dim-2) directly? # edges: [B, N, K, 3]

I'm confused.

Hi @hancyran,

Thanks for open-sourcing your excellent work!

I notice that you reported the results of mIoU, mAcc, OA of 6-fold S3DIS in table 2, could you please share with me the iou on each category, e.g., ceiling, floor, ...? just like what you did on table 6-9 in the supplementary material.

Much appreciate it!

When I just use UmbrellaSurfaceConstructor in front of other networks, the training of the whole network becomes very slow, especially the loss stage, do you know the reason? The data size is the same as Repsurf, but the speed is much slower. Remove UmbrellaSurfaceConstructor, the speed will recover quickly.

Hello,

I'm trying to reproduce the reported results on ModelNet40. I have written a data loader for ModelNet40 and I'm training it with all the implementation details in the paper (Appendix G). The maximum accuracy I'm getting is 92.8 which is way less than the reported numbers.

Can you upload the code for ModelNet40?