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arXiv Computer Vision By Haodi Yao, Fenghua He, Ning Hao, Yao Su

CLIDD: Cross-Layer Independent Deformable Description for Efficient and Discriminative Local Feature Representation

Read the original on arXiv Computer Vision →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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