Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
169 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Residual Learning for Image Point Descriptors (2312.15471v1)

Published 24 Dec 2023 in cs.CV and cs.RO

Abstract: Local image feature descriptors have had a tremendous impact on the development and application of computer vision methods. It is therefore unsurprising that significant efforts are being made for learning-based image point descriptors. However, the advantage of learned methods over handcrafted methods in real applications is subtle and more nuanced than expected. Moreover, handcrafted descriptors such as SIFT and SURF still perform better point localization in Structure-from-Motion (SfM) compared to many learned counterparts. In this paper, we propose a very simple and effective approach to learning local image descriptors by using a hand-crafted detector and descriptor. Specifically, we choose to learn only the descriptors, supported by handcrafted descriptors while discarding the point localization head. We optimize the final descriptor by leveraging the knowledge already present in the handcrafted descriptor. Such an approach of optimization allows us to discard learning knowledge already present in non-differentiable functions such as the hand-crafted descriptors and only learn the residual knowledge in the main network branch. This offers 50X convergence speed compared to the standard baseline architecture of SuperPoint while at inference the combined descriptor provides superior performance over the learned and hand-crafted descriptors. This is done with minor increase in the computations over the baseline learned descriptor. Our approach has potential applications in ensemble learning and learning with non-differentiable functions. We perform experiments in matching, camera localization and Structure-from-Motion in order to showcase the advantages of our approach.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (52)
  1. David G Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91–110, 2004.
  2. Surf: Speeded up robust features. In ECCV, 2006.
  3. Orb: An efficient alternative to sift or surf. In ICCV, 2011.
  4. Kaze features. In ECCV, 2012.
  5. Brief: Binary robust independent elementary features. In ECCV, 2010.
  6. Daisy: An efficient dense descriptor applied to wide-baseline stereo. T-PAMI, 32(5):815–830, 2009.
  7. Superpoint: Self-supervised interest point detection and description. In CVPRW, 2018.
  8. Structure-from-motion revisited. In CVPR, 2016.
  9. Modeling the world from internet photo collections. International journal of computer vision, 80:189–210, 2008.
  10. Changchang Wu. Towards linear-time incremental structure from motion. In 2013 International Conference on 3D Vision-3DV 2013, pages 127–134. IEEE, 2013.
  11. Orb-slam: a versatile and accurate monocular slam system. IEEE T-RO, 31(5):1147–1163, 2015.
  12. Monoslam: Real-time single camera slam. T-PAMI, 29(6):1052–1067, 2007.
  13. An experimental comparison of ros-compatible stereo visual slam methods for planetary rovers. In 2018 5th IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace), pages 386–391. IEEE, 2018.
  14. Multibody structure-from-motion in practice. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(6):1134–1141, 2010.
  15. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99–106, 2021.
  16. Neural 3d scene reconstruction with the manhattan-world assumption. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5511–5520, 2022.
  17. Indexing based on scale invariant interest points. In Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, volume 1, pages 525–531. IEEE, 2001.
  18. A combined corner and edge detector. In Alvey vision conference, volume 15, pages 10–5244, 1988.
  19. Machine learning for high-speed corner detection. In Computer Vision–ECCV 2006: 9th European Conference on Computer Vision, Graz, Austria, May 7-13, 2006. Proceedings, Part I 9, pages 430–443. Springer, 2006.
  20. Multi-task self-supervised visual learning. In Proceedings of the IEEE international conference on computer vision, pages 2051–2060, 2017.
  21. Deep learning face representation by joint identification-verification. Advances in neural information processing systems, 27, 2014.
  22. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597–1607. PMLR, 2020.
  23. Microsoft coco: Common objects in context. In ECCV, 2014.
  24. R2d2: repeatable and reliable detector and descriptor. In NeurIPS, 2019.
  25. Neural outlier rejection for self-supervised keypoint learning. In ICLR, 2020.
  26. Matchformer: Interleaving attention in transformers for feature matching. In Proceedings of the Asian Conference on Computer Vision (ACCV), pages 2746–2762, December 2022.
  27. Comparative evaluation of hand-crafted and learned local features. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1482–1491, 2017.
  28. Image matching across wide baselines: From paper to practice. International Journal of Computer Vision, 129(2):517–547, 2021.
  29. Disk: Learning local features with policy gradient. Advances in Neural Information Processing Systems, 33:14254–14265, 2020.
  30. Universal correspondence network. Advances in neural information processing systems, 29, 2016.
  31. Working hard to know your neighbor’s margins: Local descriptor learning loss. Advances in neural information processing systems, 30, 2017.
  32. Cross-descriptor visual localization and mapping. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6058–6067, 2021.
  33. Loftr: Detector-free local feature matching with transformers. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8922–8931, 2021.
  34. Probabilistic warp consistency for weakly-supervised semantic correspondences. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8708–8718, 2022.
  35. Tilde: A temporally invariant learned detector. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5279–5288, 2015.
  36. Lift: Learned invariant feature transform. In ECCV, 2016.
  37. Learning covariant feature detectors. In Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III 14, pages 100–117. Springer, 2016.
  38. Learning to detect features in texture images. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6325–6333, 2018.
  39. Learning correspondence uncertainty via differentiable nonlinear least squares. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13102–13112, 2023.
  40. Self-supervised equivariant learning for oriented keypoint detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4847–4857, 2022.
  41. Dong-Hyun Lee et al. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In Workshop on challenges in representation learning, ICML, volume 3, page 896, 2013.
  42. Zippypoint: Fast interest point detection, description, and matching through mixed precision discretization. arXiv preprint arXiv:2203.03610, 2022.
  43. Three things everyone should know to improve object retrieval. In 2012 IEEE conference on computer vision and pattern recognition, pages 2911–2918. IEEE, 2012.
  44. Dualnet: Learn complementary features for image recognition. In Proceedings of the IEEE International conference on computer vision, pages 502–510, 2017.
  45. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2015.
  46. Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5173–5182, 2017.
  47. Benchmarking 6dof outdoor visual localization in changing conditions. In CVPR, 2018.
  48. Image retrieval for image-based localization revisited. In BMVC, 2012.
  49. From coarse to fine: Robust hierarchical localization at large scale. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12716–12725, 2019.
  50. Netvlad: Cnn architecture for weakly supervised place recognition. In CVPR, 2016.
  51. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381–395, 1981.
  52. Two-view geometry estimation unaffected by a dominant plane. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), volume 1, pages 772–779 vol. 1, 2005.

Summary

  • The paper presents a hybrid approach that fuses handcrafted keypoint detection with deep residual learning to enhance descriptor precision.
  • It employs a two-step process—first detecting keypoints with methods like SIFT, then using DNNs to learn residual patterns for sub-pixel localization.
  • Experiments demonstrate superior performance over traditional and fully learned methods in matching and camera localization tasks with faster training convergence.

Introduction

Local image feature descriptors are crucial in computer vision, with applications ranging from Structure-from-Motion (SfM) to Simultaneous Localization and Mapping (SLAM). They are generally classified into handcrafted descriptors, like SIFT and SURF, and learned descriptors obtained through methods such as deep learning. While learned descriptors benefit from advancements in self-supervised learning and neural networks, they often fall short in precise point localization compared to handcrafted ones. In response, this paper introduces a hybrid method that combines the strength of handcrafted descriptors in point localization with the power of deep learning to learn the residual knowledge beyond what's captured by handcrafted methods.

Related Work and Challenges

Prevailing approaches for local image point description are to fully learn both keypoints and descriptors or improve upon handcrafted methods. Self-supervised learning offers an avenue to train descriptors by using image augmentations, but it presents challenges in sub-pixel point localization. This issue of low ‘resolution’ in point clouds impacts the quality of 3D reconstruction in SfM. Existing methods like the SuperPoint network mitigate these problems to some extent but require high computational resources, rendering them less suitable for real-time or resource-constrained applications.

Methodology

The proposed method focuses on learning a descriptor using deep neural networks (DNNs) conditioned on keypoints detected by a handcrafted method. This technique involves a two-step process where first, a handcrafted method like SIFT or SURF is applied to detect keypoints. Then, a DNN refines these keypoints by learning additional characteristics not captured by the initial method. This fusion of handcrafted precision with DNN's ability to learn nuanced patterns enables more accurate and reliable descriptors that are still computationally efficient. The combined descriptors are optimized through self-supervised training on the MS COCO dataset with metric learning, and extensive evaluations are performed on various benchmarks.

Experiments and Conclusions

The hybrid method outperforms handcrafted methods and the SuperPoint baseline across numerous metrics on matching and camera localization tasks. Its key advantage is learning only what’s missing in the handcrafted descriptors, enabling it to add meaningful information. This distinction allows for faster convergence during training compared to fully learned counterparts. While the method introduces an overhead due to the DNN, it offers a balanced tradeoff between computational efficiency and performance for image descriptors. In closing, the paper demonstrates how machine learning can complement traditional techniques to push the boundaries of what's possible in computer vision, paving a way for more sophisticated yet efficient algorithms.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets