By leveraging a sparse-to-dense matching paradigm, we cast the correspondence learning problem as a supervised classification task to learn to output highly peaked correspondence maps. We show that S2DNet achieves state-of-the-art results on the HPatches benchmark, as well as on several long-...
Second, we utilize sparse-to-dense matching with symmetry of correspondence to implement accurate pixel-level matching, which enables our method to produce more accurate correspondences. Result Experiments show that our proposed DSD-MatchingNet achieves a better performance on the image matching bench...
By leveraging a sparse-to-dense matching paradigm, we cast the correspondence learning problem as a supervised classification task to learn to output highly peaked correspondence maps. We show that S2DNet achieves state-of-the-art results on the HPatches benchmark, as well as on several long-...
@inproceedings{peng2021sparse, title={Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation}, author={Peng, Duo and Lei, Yinjie and Li, Wen and Zhang, Pingping and Guo, Yulan}, booktitle={Proceedings of the International...
While these methods [34, 13] have shown impressive results, they are still incompetent on low-textured, specular, and reflec- tive regions where local features are not discriminative for matching. Recent work [20, 43, 22, 23] shows tha...
Finally, the enhanced features based on spatial consistency are repeatedly fed into the sparse-to-dense matching module to rebuild reliable correspondence, and the optimal transformation parameter is re-estimated for final alignment. Our experiments show that, with the proposed method, the inlier ratio...
Matching the ground-truth and predicted bounding box class labels: After it is determined that an object has been successfully detected in step 1, class label of the predicted bounding box is matched to the ground-truth bounding box accordingly. Different metrics are used to evaluate an object ...
MatchingNormal segmentation of geometric range data has been a common practice integrated in the building blocks of point cloud registration. Most well-known point to plane and plane to plane state-of-the-art registration techniques make use of normal features to ensure a better alignment. However...
Once we found feature points in key frames are not in the global frame, add them into the globalpoint clouds.PTAM: Parallel Tracking and Mapping for Small AR Workspaces Tracking is a two-step process: A coarse-to-fine feature matching to estimate a initial camera pose; Use loc...
However, the current methods cannot tackle the multi-view matching problem well because of the dependence on the accurate camera pose as well as the matching uncertainty. In this paper, to handle these issues, a new sparse-to-dense diffusion framework is put forward. First, the scene ...