Graph Context Transformation Learning for Progressive Correspondence Pruning (2312.15971v1)
Abstract: Most of existing correspondence pruning methods only concentrate on gathering the context information as much as possible while neglecting effective ways to utilize such information. In order to tackle this dilemma, in this paper we propose Graph Context Transformation Network (GCT-Net) enhancing context information to conduct consensus guidance for progressive correspondence pruning. Specifically, we design the Graph Context Enhance Transformer which first generates the graph network and then transforms it into multi-branch graph contexts. Moreover, it employs self-attention and cross-attention to magnify characteristics of each graph context for emphasizing the unique as well as shared essential information. To further apply the recalibrated graph contexts to the global domain, we propose the Graph Context Guidance Transformer. This module adopts a confident-based sampling strategy to temporarily screen high-confidence vertices for guiding accurate classification by searching global consensus between screened vertices and remaining ones. The extensive experimental results on outlier removal and relative pose estimation clearly demonstrate the superior performance of GCT-Net compared to state-of-the-art methods across outdoor and indoor datasets. The source code will be available at: https://github.com/guobaoxiao/GCT-Net/.
- MAGSAC: marginalizing sample consensus. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10197–10205.
- Matching with PROSAC-progressive sample consensus. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 1, 220–226. IEEE.
- Two-view geometry estimation unaffected by a dominant plane. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 1, 772–779. IEEE.
- MS2DG-Net: Progressive correspondence learning via multiple sparse semantics dynamic graph. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8973–8982.
- Attentional feature fusion. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 3560–3569.
- Superpoint: Self-supervised interest point detection and description. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 224–236.
- An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.
- Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6): 381–395.
- Coordinate attention for efficient mobile network design. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13713–13722.
- Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7132–7141.
- Eslam: Efficient dense slam system based on hybrid representation of signed distance fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 17408–17419.
- Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
- Segment anything. arXiv preprint arXiv:2304.02643.
- Feature dynamic alignment and refinement for infrared–visible image fusion: Translation robust fusion. Information Fusion, 95: 26–41.
- Progressive Neighbor Consistency Mining for Correspondence Pruning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9527–9537.
- Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 10012–10022.
- Lowe, D. G. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60: 91–110.
- Image matching from handcrafted to deep features: A survey. International Journal of Computer Vision, 129: 23–79.
- Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 652–660.
- PointNet++ deep hierarchical feature learning on point sets in a metric space. In Proceedings of the 31st International Conference on Neural Information Processing Systems, 5105–5114.
- Deep fundamental matrix estimation. In Proceedings of the European Conference on Computer Vision, 284–299.
- Pixel-Perfect Structure-From-Motion With Featuremetric Refinement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–13.
- Acne: Attentive context normalization for robust permutation-equivariant learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 11286–11295.
- Rethinking the necessity of image fusion in high-level vision tasks: A practical infrared and visible image fusion network based on progressive semantic injection and scene fidelity. Information Fusion, 101870.
- MLESAC: A new robust estimator with application to estimating image geometry. Computer vision and Image Understanding, 78(1): 138–156.
- Attention is all you need. Advances in Neural Information Processing Systems, 30: 1–11.
- Cbam: Convolutional block attention module. In Proceedings of the European Conference on Computer Vision, 3–19.
- Learning to find good correspondences. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2666–2674.
- Learning two-view correspondences and geometry using order-aware network. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 5845–5854.
- ConvMatch: Rethinking Network Design for Two-View Correspondence Learning. In Proc. AAAI Conf. Artif. Intell, 1–12.
- Progressive correspondence pruning by consensus learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 6464–6473.
- MSA-Net: Establishing reliable correspondences by multiscale attention network. IEEE Transactions on Image Processing, 31: 4598–4608.
- T-Net: Effective permutation-equivariant network for two-view correspondence learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 1950–1959.