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How do i convert the matched keypoints to 3d landmarks after apploying superglue matching ? Such that they can be converted into pointcloud and saved to database?

我想问一下这个AvgError的精度跟显存有关系吗？我的是TITAN XP 12G的显存

RuntimeError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 6.00 GiB total capacity; 4.08 GiB already allocated; 10.56 MiB free; 4.18 GiB reserved in total by PyTorch)

hi,progessor:
like the topic said,please help!

请问有试过在TUM RGBD fr3 long office那个序列上跑吗？
我跑出来结果非常差，各种方法都得不到一个合理的轨迹，不知道是不是存在问题还是这个序列本身太难。

when i run main.py in TUM dataset, only one red dot is shown in the plot, and the trajectory is not shown.
thank you！

can this part be more specific?

您好，我在运行的时候，会报错UnboundLocalError: local variable 'img2' referenced before assignment，我在前面加了global img2 也没用，我的Python版本是3.7，请问您知道怎么解决吗？

Thank you for your good work. I encountered this problem when used
$ python main.py

Traceback (most recent call last):
File "main.py", line 124, in
run(args)
File "main.py", line 110, in run
cv2.imwrite("results/" + fname + '_new.png', img2)
UnboundLocalError: local variable 'img2' referenced before assignment

then,I put cv2.imwrite() into for()
it works ,but the four methods all just load the model,like this
INFO:root:{'descriptor_dim': 256, 'weights': 'outdoor', 'keypoint_encoder': [32, 64, 128, 256], 'GNN_layers': ['self', 'cross', 'self', 'cross', 'self', 'cross', 'self', 'cross', 'self', 'cross', 'self', 'cross', 'self', 'cross', 'self', 'cross', 'self', 'cross'], 'sinkhorn_iterations': 100, 'match_threshold': 0.2, 'cuda': 1, 'type': 'SuperGlue'}
INFO:root:creating SuperGlue matcher...
Loaded SuperGlue model ("outdoor" weights)

Thank you.

Hello, may I ask, how to output the matching results of image feature points?

When I try running the result_eval.py, I get the following error:
result_eval.py:21: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
return np.array(img_ids), np.array(est_points), np.array(gt_points)
Traceback (most recent call last):
File "result_eval.py", line 70, in
main()
File "result_eval.py", line 38, in main
errors = np.linalg.norm((gt_xyz - est_xyz), axis=1)
ValueError: operands could not be broadcast together with shapes (8480,) (8480,3)

您好，运行程序后，有以下两个问题想请教您：
1. 最后生成的AvgError的图只有一个红点，并未有完整的轨迹，是什么问题？
2. 我是否可以用我自己的图片在此程序上进行图像匹配，请问groundtruth文件怎样能获得？

您好，请问有尝试复现superglue文章里面的sift+superglue吗？最近一直在尝试，一直没有进展，希望可以交流

orb特征点的结果真的有这么差吗，01序列甚至无法完全跑完就报错了

how to get this visualization in the code? Thank you.

I try to run code in TUM datasets but get the error message.

Traceback (most recent call last):
File "main.py", line 109, in
run(args)
File "main.py", line 65, in run
loader = create_dataloader(config["dataset"])
File "/home/zhy/PyProject/Python-VO/DataLoader/init.py", line 9, in create_dataloader
loader = eval(code_line)
File "", line 1, in
File "/home/zhy/PyProject/Python-VO/DataLoader/TUMRGBLoader.py", line 32, in init
self.read_imgs()
File "/home/zhy/PyProject/Python-VO/DataLoader/TUMRGBLoader.py", line 76, in read_imgs
rgb_list = lines[:, 1]
IndexError: too many indices for array

I use this code for running:
python main.py --config params/tumrgb_superpoint_supergluematch.yaml

The TUM dataset is organized as follows:
├ TUM
└rgbd_dataset_freiburg1_360
   └── depth
└ ...
   └── rgb
└ ...
   └── depth.txt
   └── rgb.txt
   └── rgbd_gt.txt

How to fix this error?