haozheqi / p2b Goto Github PK

def getPCandBBfromPandas(self, box, calib):
center = [box["x"], box["y"] - box["height"] / 2, box["z"]]
size = [box["width"], box["length"], box["height"]]
orientation = Quaternion(
axis=[0, 1, 0], radians=box["rotation_y"]) * Quaternion(
axis=[1, 0, 0], radians=np.pi / 2)
Using this function to convert point cloud coordinates to camera coordinates,the orientation use to move the cut point cloud to the origin of the camera coordinate system and turn it in a positive direction,I think orientation = Quaternion(axis=[0, 1, 0], radians=box["rotation_y"]) is ok,so why you choose to multiply Quaternion(axis=[1, 0, 0], radians=np.pi / 2)

Hello, thanks for your good job.
I noticed that you mentioned the running speed on a single NVIDIA 1080Ti GPU. Can you share the training time and what machines are used?

Hi, @HaozheQi thanks for your great work! I have a few questions after reading your code carefully.

1. Why did you randomly sample 128 points through FPS when selecting seed, but when calculating loss, you used the first 128 points of the input point cloud as ground truth? I know this question is already appear in #Issue1, but I am still confused in this question.

2. How do you define the ground truth of heading angle (rotation of x-y plane), which cannot be found in your codes clearly but is mentioned in your paper.

Looking forward your reply!

Hi, Thanks for your graet work!
When I execute this command, it has the following problem
RuntimeError: Error compiling objects for extension

Cuda 11.1
Pytorch 1.8.0
RTX3090

Can you provide a docker image for running?

Good job!
Can you release models of other categories? Thank you very much.

Hi, thank you for your good work.
I try to visualize the model output of PCB.
I employ mayavi to visualize candidate_PCs and BBs[I] in test_tracking.pyhttps://github.com/HaozheQi/P2B/blob/ccac70ffad40a1f9b21c617223bfd0c04e2d4359/test_tracking.py.
But there were some problems with the results, the box didn't align with the point cloud.
Is there any transformation involved?

I look forward to your reply.

Hi, @StiphyJay

Hello, thanks for your good job.
The figure in the paper shows the effect of P2b very well(such as Fig. 7 9 10). What software do you use to visualize these point clouds and boxes? (mayavi? meshlab?)

When I run the code ,I meet a questions.
RuntimeError: Expected isFloatingType(grads[i].scalar_type()) to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.) (validate_outputs at ..\torch\csrc\autograd\engine.cpp:476)
(no backtrace available)
I don't know how to solve it.I would appreciate it if you could help me.

Hello, thanks for your good job. I have a question about this line in your code:

Why we can just take the first 128 numbers in the target mask as seed targetness label?

pointnet2/_ext-src/include/utils.h:23:5: error: ‘AT_CHECK’ was not declared in this scope
AT_CHECK(x.scalar_type() == at::ScalarType::Float, \

pointnet2/_ext-src/src/sampling.cpp:82:5: error: ‘AT_CHECK’ was not declared in this scope
AT_CHECK(false, "CPU not supported");

error: command 'x86_64-linux-gnu-gcc' failed with exit status 1

Dear @HaozheQi ,

Thanks for your excellent work ! Now I am trying to reproduce the results through the code you provided, but I got this error:

Traceback (most recent call last):
File "/home/gjt/.pycharm_helpers/pydev/pydevd.py", line 1668, in
main()
File "/home/gjt/.pycharm_helpers/pydev/pydevd.py", line 1662, in main
globals = debugger.run(setup['file'], None, None, is_module)
File "/home/gjt/.pycharm_helpers/pydev/pydevd.py", line 1072, in run
pydev_imports.execfile(file, globals, locals) # execute the script
File "/home/gjt/.pycharm_helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "/home/gjt/P2B/train_tracking.py", line 165, in
loss.backward()
File "/home/gjt/anaconda3/envs/P2B/lib/python3.6/site-packages/torch/tensor.py", line 166, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "/home/gjt/anaconda3/envs/P2B/lib/python3.6/site-packages/torch/autograd/init.py", line 99, in backward
allow_unreachable=True) # allow_unreachable flag

RuntimeError: Expected isFloatingType(grads[i].type().scalarType()) to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)

My environment is:
python 3.6.9
pytorch 1.3.1
torchvision 0.4.2
cudatoolkit 10.0.30
cudnn 7.6.5
h5py 2.10.0
numpy 1.17.4
pprint 0.1
enum34 1.1.10
future 0.18.2
pandas 0.25.3
shapely 1.7b1
matplotlib 3.1.2
pomegranate 0.13.3
ipykernel 5.1.3.0
jupyter 1.0.0
imageio 2.6.1
pyquaternion 0.9.5

Do you know what's wrong with it? Do you know what's wrong with it? Looking forward to hearing from you. Thanks for your excellent work again !

Hello, thanks for you job.
I want to know that weather the model(netR_36.model) is trained in two GTX1080Ti gpu? if you are, when i want to test this model in a machine with one GTX1080Ti, this model could weather or not work? When i test it, there are some error in my terminal.
I have modified the code in test_tracking.py when i test.
parser.add_argument('--ngpu', type=int, default=1, help='# GPUs')
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
the error is:
Traceback (most recent call last):
File "test_tracking.py", line 177, in
netR.load_state_dict(torch.load(os.path.join(args.save_root_dir, args.model)))
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 845, in load_state_dict
self.class.name, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for Pointnet_Tracking:

I would appreciate it if you could reply. Thank you!

hello, sorry to bother you.i wonder why the pomegranate is not satisfied with python3.6 because of the version of numpy

Hello, I have a doubt about the implementation of distanceBB_Gaussian
def distanceBB_Gaussian(box1, box2, sigma=1): off1 = np.array([ box1.center[0], box1.center[2], Quaternion(matrix=box1.rotation_matrix).degrees ]) off2 = np.array([ box2.center[0], box2.center[2], Quaternion(matrix=box2.rotation_matrix).degrees ]) dist = np.linalg.norm(off1 - off2) score = np.exp(-0.5 * dist / (sigma * sigma)) return score
Why do you use box1.center[0] and box1.center[2] that is x and z?
Why not use box1.center[0] and box1.center[1] that is x and y?

Hello, @HaozheQi ,thanks for your great job!
I'm running your codes for my own interests, I successfully used the model you provided（netR_36.pth） to reproduce the results of the paper.Then I try to tranin the model by myself. But I ran into some problems when running your train_tracking.py .The problems are as follows:

/home/zhuhu/anaconda3/envs/P2B/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:122: UserWarning: Detected call of lr_scheduler.step() before optimizer.step(). In PyTorch 1.1.0 and later, you should call them in the opposite order: optimizer.step() before lr_scheduler.step(). Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning)
======>>>>> Online epoch: #0, lr=0.001000 <<<<<======
0%| | 0/21 [00:02<?, ?it/s]
Traceback (most recent call last):
File "train_tracking.py", line 181, in
loss.backward()
File "/home/zhuhu/anaconda3/envs/P2B/lib/python3.6/site-packages/torch/tensor.py", line 195, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "/home/zhuhu/anaconda3/envs/P2B/lib/python3.6/site-packages/torch/autograd/init.py", line 99, in backward
allow_unreachable=True) # allow_unreachable flag
RuntimeError: Expected isFloatingType(grads[i].type().scalarType()) to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)

I'm not sure how to solve this problem, hope to get your reply!

hi! when i run test_tracking.py,the following error occurred：

Namespace(IoU_Space=3, category_name='Car', data_dir='./data/training', model='netR_36.pth', model_fusion='pointcloud', ngpu=1, reference_BB='previous_result', save_root_dir='./model/car_model/', shape_aggregation='firstandprevious')
Traceback (most recent call last):
File "/home/wangkai/P2B/test_tracking.py", line 176, in
netR.load_state_dict(torch.load(os.path.join(args.save_root_dir, args.model)))
File "/home/wangkai/anaconda3/envs/py1/lib/python3.6/site-packages/torch/nn/modules/module.py", line 777, in load_state_dict
self.class.name, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for Pointnet_Tracking:
Missing key(s) in state_dict: "backbone_net.SA_modules.0.mlps.0.layer0.conv.weight", "backbone_net.SA_modules.0.mlps.0.layer0.normlayer.bn.weight", "backbone_net.SA_modules.0.mlps.0.layer0.normlayer.bn.bias", "backbone_net.SA_modules.0.mlps.0.layer0.normlayer.bn.running_mean", "backbone_net.SA_modules.0.mlps.0.layer0.normlayer.bn.running_var", "backbone_net.SA_modules.0.mlps.0.layer1.conv.weight", "backbone_net.SA_modules.0.mlps.0.layer1.normlayer.bn.weight", "backbone_net.SA_modules.0.mlps.0.layer1.normlayer.bn.bias", "backbone_net.SA_modules.0.mlps.0.layer1.normlayer.bn.running_mean", "backbone_net.SA_modules.0.mlps.0.layer1.normlayer.bn.running_var", "backbone_net.SA_modules.0.mlps.0.layer2.conv.weight", "backbone_net.SA_modules.0.mlps.0.layer2.normlayer.bn.weight", "backbone_net.SA_modules.0.mlps.0.layer2.normlayer.bn.bias", "backbone_net.SA_modules.0.mlps.0.layer2.normlayer.bn.running_mean", "backbone_net.SA_modules.0.mlps.0.layer2.normlayer.bn.running_var", "backbone_net.SA_modules.1.mlps.0.layer0.conv.weight", "backbone_net.SA_modules.1.mlps.0.layer0.normlayer.bn.weight", "backbone_net.SA_modules.1.mlps.0.layer0.normlayer.bn.bias", "backbone_net.SA_modules.1.mlps.0.layer0.normlayer.bn.running_mean", "backbone_net.SA_modules.1.mlps.0.layer0.normlayer.bn.running_var", "backbone_net.SA_modules.1.mlps.0.layer1.conv.weight", "backbone_net.SA_modules.1.mlps.0.layer1.normlayer.bn.weight", "backbone_net.SA_modules.1.mlps.0.layer1.normlayer.bn.bias", "backbone_net.SA_modules.1.mlps.0.layer1.normlayer.bn.running_mean", "backbone_net.SA_modules.1.mlps.0.layer1.normlayer.bn.running_var", "backbone_net.SA_modules.1.mlps.0.layer2.conv.weight", "backbone_net.SA_modules.1.mlps.0.layer2.normlayer.bn.weight", "backbone_net.SA_modules.1.mlps.0.layer2.normlayer.bn.bias", "backbone_net.SA_modules.1.mlps.0.layer2.normlayer.bn.running_mean", "backbone_net.SA_modules.1.mlps.0.layer2.normlayer.bn.running_var", "backbone_net.SA_modules.2.mlps.0.layer0.conv.weight", "backbone_net.SA_modules.2.mlps.0.layer0.normlayer.bn.weight", "backbone_net.SA_modules.2.mlps.0.layer0.normlayer.bn.bias", "backbone_net.SA_modules.2.mlps.0.layer0.normlayer.bn.running_mean", "backbone_net.SA_modules.2.mlps.0.layer0.normlayer.bn.running_var", "backbone_net.SA_modules.2.mlps.0.layer1.conv.weight", "backbone_net.SA_modules.2.mlps.0.layer1.normlayer.bn.weight", "backbone_net.SA_modules.2.mlps.0.layer1.normlayer.bn.bias", "backbone_net.SA_modules.2.mlps.0.layer1.normlayer.bn.running_mean", "backbone_net.SA_modules.2.mlps.0.layer1.normlayer.bn.running_var", "backbone_net.SA_modules.2.mlps.0.layer2.conv.weight", "backbone_net.SA_modules.2.mlps.0.layer2.normlayer.bn.weight", "backbone_net.SA_modules.2.mlps.0.layer2.normlayer.bn.bias", "backbone_net.SA_modules.2.mlps.0.layer2.normlayer.bn.running_mean", "backbone_net.SA_modules.2.mlps.0.layer2.normlayer.bn.running_var", "backbone_net.cov_final.weight", "backbone_net.cov_final.bias", "mlp.layer0.conv.weight", "mlp.layer0.normlayer.bn.weight", "mlp.layer0.normlayer.bn.bias", "mlp.layer0.normlayer.bn.running_mean", "mlp.layer0.normlayer.bn.running_var", "mlp.layer1.conv.weight", "mlp.layer1.normlayer.bn.weight", "mlp.layer1.normlayer.bn.bias", "mlp.layer1.normlayer.bn.running_mean", "mlp.layer1.normlayer.bn.running_var", "mlp.layer2.conv.weight", "mlp.layer2.normlayer.bn.weight", "mlp.layer2.normlayer.bn.bias", "mlp.layer2.normlayer.bn.running_mean", "mlp.layer2.normlayer.bn.running_var", "FC_layer_cla.0.conv.weight", "FC_layer_cla.0.normlayer.bn.weight", "FC_layer_cla.0.normlayer.bn.bias", "FC_layer_cla.0.normlayer.bn.running_mean", "FC_layer_cla.0.normlayer.bn.running_var", "FC_layer_cla.1.conv.weight", "FC_layer_cla.1.normlayer.bn.weight", "FC_layer_cla.1.normlayer.bn.bias", "FC_layer_cla.1.normlayer.bn.running_mean", "FC_layer_cla.1.normlayer.bn.running_var", "FC_layer_cla.2.conv.weight", "FC_layer_cla.2.conv.bias", "fea_layer.0.conv.weight", "fea_layer.0.normlayer.bn.weight", "fea_layer.0.normlayer.bn.bias", "fea_layer.0.normlayer.bn.running_mean", "fea_layer.0.normlayer.bn.running_var", "fea_layer.1.conv.weight", "fea_layer.1.conv.bias", "vote_layer.0.conv.weight", "vote_layer.0.normlayer.bn.weight", "vote_layer.0.normlayer.bn.bias", "vote_layer.0.normlayer.bn.running_mean", "vote_layer.0.normlayer.bn.running_var", "vote_layer.1.conv.weight", "vote_layer.1.normlayer.bn.weight", "vote_layer.1.normlayer.bn.bias", "vote_layer.1.normlayer.bn.running_mean", "vote_layer.1.normlayer.bn.running_var", "vote_layer.2.conv.weight", "vote_layer.2.conv.bias", "vote_aggregation.mlps.0.layer0.conv.weight", "vote_aggregation.mlps.0.layer0.normlayer.bn.weight", "vote_aggregation.mlps.0.layer0.normlayer.bn.bias", "vote_aggregation.mlps.0.layer0.normlayer.bn.running_mean", "vote_aggregation.mlps.0.layer0.normlayer.bn.running_var", "vote_aggregation.mlps.0.layer1.conv.weight", "vote_aggregation.mlps.0.layer1.normlayer.bn.weight", "vote_aggregation.mlps.0.layer1.normlayer.bn.bias", "vote_aggregation.mlps.0.layer1.normlayer.bn.running_mean", "vote_aggregation.mlps.0.layer1.normlayer.bn.running_var", "vote_aggregation.mlps.0.layer2.conv.weight", "vote_aggregation.mlps.0.layer2.normlayer.bn.weight", "vote_aggregation.mlps.0.layer2.normlayer.bn.bias", "vote_aggregation.mlps.0.layer2.normlayer.bn.running_mean", "vote_aggregation.mlps.0.layer2.normlayer.bn.running_var", "FC_proposal.0.conv.weight", "FC_proposal.0.normlayer.bn.weight", "FC_proposal.0.normlayer.bn.bias", "FC_proposal.0.normlayer.bn.running_mean", "FC_proposal.0.normlayer.bn.running_var", "FC_proposal.1.conv.weight", "FC_proposal.1.normlayer.bn.weight", "FC_proposal.1.normlayer.bn.bias", "FC_proposal.1.normlayer.bn.running_mean", "FC_proposal.1.normlayer.bn.running_var", "FC_proposal.2.conv.weight", "FC_proposal.2.conv.bias".

Unexpected key(s) in state_dict: "module.backbone_net.SA_modules.0.mlps.0.layer0.conv.weight", "module.backbone_net.SA_modules.0.mlps.0.layer0.normlayer.bn.weight", "module.backbone_net.SA_modules.0.mlps.0.layer0.normlayer.bn.bias", "module.backbone_net.SA_modules.0.mlps.0.layer0.normlayer.bn.running_mean", "module.backbone_net.SA_modules.0.mlps.0.layer0.normlayer.bn.running_var", "module.backbone_net.SA_modules.0.mlps.0.layer0.normlayer.bn.num_batches_tracked", "module.backbone_net.SA_modules.0.mlps.0.layer1.conv.weight", "module.backbone_net.SA_modules.0.mlps.0.layer1.normlayer.bn.weight", "module.backbone_net.SA_modules.0.mlps.0.layer1.normlayer.bn.bias", "module.backbone_net.SA_modules.0.mlps.0.layer1.normlayer.bn.running_mean", "module.backbone_net.SA_modules.0.mlps.0.layer1.normlayer.bn.running_var", "module.backbone_net.SA_modules.0.mlps.0.layer1.normlayer.bn.num_batches_tracked", "module.backbone_net.SA_modules.0.mlps.0.layer2.conv.weight", "module.backbone_net.SA_modules.0.mlps.0.layer2.normlayer.bn.weight", "module.backbone_net.SA_modules.0.mlps.0.layer2.normlayer.bn.bias", "module.backbone_net.SA_modules.0.mlps.0.layer2.normlayer.bn.running_mean", "module.backbone_net.SA_modules.0.mlps.0.layer2.normlayer.bn.running_var", "module.backbone_net.SA_modules.0.mlps.0.layer2.normlayer.bn.num_batches_tracked", "module.backbone_net.SA_modules.1.mlps.0.layer0.conv.weight", "module.backbone_net.SA_modules.1.mlps.0.layer0.normlayer.bn.weight", "module.backbone_net.SA_modules.1.mlps.0.layer0.normlayer.bn.bias", "module.backbone_net.SA_modules.1.mlps.0.layer0.normlayer.bn.running_mean", "module.backbone_net.SA_modules.1.mlps.0.layer0.normlayer.bn.running_var", "module.backbone_net.SA_modules.1.mlps.0.layer0.normlayer.bn.num_batches_tracked", "module.backbone_net.SA_modules.1.mlps.0.layer1.conv.weight", "module.backbone_net.SA_modules.1.mlps.0.layer1.normlayer.bn.weight", "module.backbone_net.SA_modules.1.mlps.0.layer1.normlayer.bn.bias", "module.backbone_net.SA_modules.1.mlps.0.layer1.normlayer.bn.running_mean", "module.backbone_net.SA_modules.1.mlps.0.layer1.normlayer.bn.running_var", "module.backbone_net.SA_modules.1.mlps.0.layer1.normlayer.bn.num_batches_tracked", "module.backbone_net.SA_modules.1.mlps.0.layer2.conv.weight", "module.backbone_net.SA_modules.1.mlps.0.layer2.normlayer.bn.weight", "module.backbone_net.SA_modules.1.mlps.0.layer2.normlayer.bn.bias", "module.backbone_net.SA_modules.1.mlps.0.layer2.normlayer.bn.running_mean", "module.backbone_net.SA_modules.1.mlps.0.layer2.normlayer.bn.running_var", "module.backbone_net.SA_modules.1.mlps.0.layer2.normlayer.bn.num_batches_tracked", "module.backbone_net.SA_modules.2.mlps.0.layer0.conv.weight", "module.backbone_net.SA_modules.2.mlps.0.layer0.normlayer.bn.weight", "module.backbone_net.SA_modules.2.mlps.0.layer0.normlayer.bn.bias", "module.backbone_net.SA_modules.2.mlps.0.layer0.normlayer.bn.running_mean", "module.backbone_net.SA_modules.2.mlps.0.layer0.normlayer.bn.running_var", "module.backbone_net.SA_modules.2.mlps.0.layer0.normlayer.bn.num_batches_tracked", "module.backbone_net.SA_modules.2.mlps.0.layer1.conv.weight", "module.backbone_net.SA_modules.2.mlps.0.layer1.normlayer.bn.weight", "module.backbone_net.SA_modules.2.mlps.0.layer1.normlayer.bn.bias", "module.backbone_net.SA_modules.2.mlps.0.layer1.normlayer.bn.running_mean", "module.backbone_net.SA_modules.2.mlps.0.layer1.normlayer.bn.running_var", "module.backbone_net.SA_modules.2.mlps.0.layer1.normlayer.bn.num_batches_tracked", "module.backbone_net.SA_modules.2.mlps.0.layer2.conv.weight", "module.backbone_net.SA_modules.2.mlps.0.layer2.normlayer.bn.weight", "module.backbone_net.SA_modules.2.mlps.0.layer2.normlayer.bn.bias", "module.backbone_net.SA_modules.2.mlps.0.layer2.normlayer.bn.running_mean", "module.backbone_net.SA_modules.2.mlps.0.layer2.normlayer.bn.running_var", "module.backbone_net.SA_modules.2.mlps.0.layer2.normlayer.bn.num_batches_tracked", "module.backbone_net.cov_final.weight", "module.backbone_net.cov_final.bias", "module.mlp.layer0.conv.weight", "module.mlp.layer0.normlayer.bn.weight", "module.mlp.layer0.normlayer.bn.bias", "module.mlp.layer0.normlayer.bn.running_mean", "module.mlp.layer0.normlayer.bn.running_var", "module.mlp.layer0.normlayer.bn.num_batches_tracked", "module.mlp.layer1.conv.weight", "module.mlp.layer1.normlayer.bn.weight", "module.mlp.layer1.normlayer.bn.bias", "module.mlp.layer1.normlayer.bn.running_mean", "module.mlp.layer1.normlayer.bn.running_var", "module.mlp.layer1.normlayer.bn.num_batches_tracked", "module.mlp.layer2.conv.weight", "module.mlp.layer2.normlayer.bn.weight", "module.mlp.layer2.normlayer.bn.bias", "module.mlp.layer2.normlayer.bn.running_mean", "module.mlp.layer2.normlayer.bn.running_var", "module.mlp.layer2.normlayer.bn.num_batches_tracked", "module.FC_layer_cla.0.conv.weight", "module.FC_layer_cla.0.normlayer.bn.weight", "module.FC_layer_cla.0.normlayer.bn.bias", "module.FC_layer_cla.0.normlayer.bn.running_mean", "module.FC_layer_cla.0.normlayer.bn.running_var", "module.FC_layer_cla.0.normlayer.bn.num_batches_tracked", "module.FC_layer_cla.1.conv.weight", "module.FC_layer_cla.1.normlayer.bn.weight", "module.FC_layer_cla.1.normlayer.bn.bias", "module.FC_layer_cla.1.normlayer.bn.running_mean", "module.FC_layer_cla.1.normlayer.bn.running_var", "module.FC_layer_cla.1.normlayer.bn.num_batches_tracked", "module.FC_layer_cla.2.conv.weight", "module.FC_layer_cla.2.conv.bias", "module.fea_layer.0.conv.weight", "module.fea_layer.0.normlayer.bn.weight", "module.fea_layer.0.normlayer.bn.bias", "module.fea_layer.0.normlayer.bn.running_mean", "module.fea_layer.0.normlayer.bn.running_var", "module.fea_layer.0.normlayer.bn.num_batches_tracked", "module.fea_layer.1.conv.weight", "module.fea_layer.1.conv.bias", "module.vote_layer.0.conv.weight", "module.vote_layer.0.normlayer.bn.weight", "module.vote_layer.0.normlayer.bn.bias", "module.vote_layer.0.normlayer.bn.running_mean", "module.vote_layer.0.normlayer.bn.running_var", "module.vote_layer.0.normlayer.bn.num_batches_tracked", "module.vote_layer.1.conv.weight", "module.vote_layer.1.normlayer.bn.weight", "module.vote_layer.1.normlayer.bn.bias", "module.vote_layer.1.normlayer.bn.running_mean", "module.vote_layer.1.normlayer.bn.running_var", "module.vote_layer.1.normlayer.bn.num_batches_tracked", "module.vote_layer.2.conv.weight", "module.vote_layer.2.conv.bias", "module.vote_aggregation.mlps.0.layer0.conv.weight", "module.vote_aggregation.mlps.0.layer0.normlayer.bn.weight", "module.vote_aggregation.mlps.0.layer0.normlayer.bn.bias", "module.vote_aggregation.mlps.0.layer0.normlayer.bn.running_mean", "module.vote_aggregation.mlps.0.layer0.normlayer.bn.running_var", "module.vote_aggregation.mlps.0.layer0.normlayer.bn.num_batches_tracked", "module.vote_aggregation.mlps.0.layer1.conv.weight", "module.vote_aggregation.mlps.0.layer1.normlayer.bn.weight", "module.vote_aggregation.mlps.0.layer1.normlayer.bn.bias", "module.vote_aggregation.mlps.0.layer1.normlayer.bn.running_mean", "module.vote_aggregation.mlps.0.layer1.normlayer.bn.running_var", "module.vote_aggregation.mlps.0.layer1.normlayer.bn.num_batches_tracked", "module.vote_aggregation.mlps.0.layer2.conv.weight", "module.vote_aggregation.mlps.0.layer2.normlayer.bn.weight", "module.vote_aggregation.mlps.0.layer2.normlayer.bn.bias", "module.vote_aggregation.mlps.0.layer2.normlayer.bn.running_mean", "module.vote_aggregation.mlps.0.layer2.normlayer.bn.running_var", "module.vote_aggregation.mlps.0.layer2.normlayer.bn.num_batches_tracked", "module.FC_proposal.0.conv.weight", "module.FC_proposal.0.normlayer.bn.weight", "module.FC_proposal.0.normlayer.bn.bias", "module.FC_proposal.0.normlayer.bn.running_mean", "module.FC_proposal.0.normlayer.bn.running_var", "module.FC_proposal.0.normlayer.bn.num_batches_tracked", "module.FC_proposal.1.conv.weight", "module.FC_proposal.1.normlayer.bn.weight", "module.FC_proposal.1.normlayer.bn.bias", "module.FC_proposal.1.normlayer.bn.running_mean", "module.FC_proposal.1.normlayer.bn.running_var", "module.FC_proposal.1.normlayer.bn.num_batches_tracked", "module.FC_proposal.2.conv.weight", "module.FC_proposal.2.conv.bias".

Process finished with exit code 1

I would appreciate it if you could reply. Thank you!

Hi, thanks for your job!

when i install dependencie, etw_pytorch_utils it not found. Bo you konw how to install it?

@HaozheQi
Kindly suggest with your framework how to compute MOTA, MOTA,MT,ML,IDS,FRAG measure similar to KITTI Tracking website?
Kindly help

Hello HaozheQi, thanks for your wonderful work.

You mentioned in this issue that you use mayavi to visualize impressive figures in you paper.

Do you have any plan to open the visualization code? Or could you give us some clues about your visualization code? Such as the reference projects?

I want to visualize the input and output results of P2B frame by frame, so I can learn the process in detail.

hi Qi,
I have a question, you get the label of L_cls about vote score.
why the label[0:128] is the labels? in your function: kitty_utils.py/regularizePCwithlabel(PC,label,reg, input_size,istrain=True)

In your paper you say"Those search area seeds located on the surface of ground-truth target are regarded as positives, and the extra as negatives." but in code is first 128' label.

Where can I access the paper associated with this repo?

Dear @HaozheQi,

I tried to contact you via email but I email could find you (rejected email from your server). Congrats for your paper accepted as an oral to CVPR'20, merging 3D Siamese network with VoteNet is a nice idea.

I went through the details of your paper and wondered how you estimated the "Running speed" in section 4.5. You claim that "SC3D in default setting ran with 1.8 FPS on the same platform.". However the original SC3D paper states the following: "Our model takes on average 1.8ms to evaluate 147 candidates." (Section 5 on Timing). Can you enlighten me where the 1.8 FPS originates from? I am afraid it might be a typo.

Thank you,

Silvio Giancola

Where are the testing results?