arXiv Computer Vision
FlexGlue: Sparse-to-Dense Feature Matching and Visual Disambiguation
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SUFLECA is a weakly supervised framework that improves zero‑shot CAD‑to‑image alignment by scaling geometry‑grounded feature learning using Normalized Object Coordinates across up to 12 real and synthetic datasets. It introduces a geometrically consistent matching algorithm that reliably establishes CAD‑to‑image correspondences, enabling accurate, sub‑second alignment without iterative pose refinement. On the ScanNet25k benchmark, SUFLECA achieves 32.8%/42.6% category/instance accuracy, outperforming the strongest zero‑shot baseline by 9.7/12.5 percentage points and surpassing existing pose‑supervised methods for the first time.
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RIPE++: Reinforced Keypoint Learning from Positive Pairs Only
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arXiv Computer Vision
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CLIDD: Cross-Layer Independent Deformable Description for Efficient and Discriminative Local Feature Representation
CLIDD (Cross-Layer Independent Deformable Description) is a local feature representation method that samples directly from independent feature hierarchies using learnable offsets, avoiding the construction of a dense feature map and thus reducing memory and computation overhead. The approach breaks interpolation-induced correlation through per-keypoint per-layer offsets, preserving discriminability even at high keypoint densities. A hardware-aware kernel fusion strategy and a scalable training framework that combine metric learning and knowledge distillation enable real-time performance, achieving superior matching accuracy and exceeding 200 FPS on edge devices.