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πŸ“ Pipeline

*** We have the following 7 steps in our pipeline:
$ prepare_images
$ compute_keypoints_descriptors
$ image_matching
$ data_feature_matching
$ compute_k_matrix
$ generate_point_cloud
$ 3D reconstruction

πŸ›οΈ Datasets

  • snow-man.
  • hammer.
  • cottage

? Something simplier and on our own terms

❌ Problems

Annoying Magic numbers

  • number of clusters while image matching.
  • Ransic threshold.

Triangulation Step looks to UnFold Object Points

Masking Regular Objects for datasets with UnMasked Images

🧐 What we noticed that caused poor feature match:

  • Blury images.
  • Change in focus.
  • Change in distance.

❓ Questions

Triangulation ?

πŸ“Š Figures and Plots

Snow-Man Points Cloud

SnowMan_Points_Cloud

Hammer Feature Matches

Good Feature Match Bad Feature Match
1_with_2 1_with_9

Hammer Feature Match Animated Video

output_1_2.mp4

Triangulation With Hand-Written Labels (Yesterday)

Hammer_Points_Cloud

Traingulation Without Hand-Written Labels (Yesterday)

Hammer_Points_Cloud

License

This project is licensed under the terms of the GNU General Public License version 3.0 (GPLv3). See the LICENSE file for details.