Missing Checkpoints about openglue

[marichka-dobko]

Hi, thank you for the kind comment.
We have released all the best models for each set of local feature extractors tested and reported in our manuscript. For example, for SuperPoint we released weights for the two best OpenGlue models: one used SuperPoint with KITTI, and the other used weights trained on the COCO dataset.
For SIFT descriptors, on the other hand, you can find two models: rotation and with scale-rotation included during training. The combination of rotation and scale showed better results in our experiments, but the difference was not severe, so we decided to include both options.

[chekirou]

I am getting very bad results with the coco version of openglue. I used this configuration :
name: 'SuperPointNetBn'

descriptor_dim: &DIM 256

parameters:

max_keypoints: 2048

descriptor_dim: *DIM

nms_kernel: 9

remove_borders_size: 4

keypoint_threshold: 0.005

weights: '/home/ostap/projects/pytorch-superpoint/logs/superpoint_coco_heat2_0/checkpoints/superPointNet_170000_checkpoint.pth.tar`

For the kitti version, the results are better but very far from the matching produced by superGlue, I used this configuration :

data:
root_path: '/datasets/extra_space2/ostap/MegaDepth'
train_list_path: '/home/ostap/projects/superglue-lightning/assets/megadepth_train_2.0.txt'
val_list_path: '/home/ostap/projects/superglue-lightning/assets/megadepth_valid_2.0.txt'
test_list_path: '/home/ostap/projects/superglue-lightning/assets/megadepth_valid_2.0.txt'

batch_size_per_gpu: 2
dataloader_workers_per_gpu: 4
target_size: [ 960, 720 ]
val_max_pairs_per_scene: 50
train_pairs_overlap: [0.15, 0.7]

inference:
match_threshold: 0.2

superglue:
descriptor_dim: &DESCRIPTOR_DIM 256
laf_to_sideinfo_method: 'none'
positional_encoding:
hidden_layers_sizes: [32, 64, 128]
output_size: *DESCRIPTOR_DIM
attention_gnn:
num_stages: 9
num_heads: 4
embed_dim: *DESCRIPTOR_DIM
attention: 'softmax'
use_offset: False
dustbin_score_init: 1.0
otp:
num_iters: 20
reg: 1.0
residual: True

features:
name: 'SuperPointNetBn'
descriptor_dim: &DIM 256
parameters:
max_keypoints: 1024
descriptor_dim: *DIM
nms_kernel: 3
remove_borders_size: 4
keypoint_threshold: 0.005
weights: '/home/hc/workspace/OpenGlue/experiment_1/superPointNet_50000_checkpoint.pth.tar'

Are these configurations the right ones ?