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AI 모델 탐색 2

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efficientloftr

zju-community

The Efficient LoFTR model was proposed in "Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed" by Yifan Wang, Xingyi He, Sida Peng, Dongli Tan, and Xiaowei Zhou from Zhejiang University. This model presents a novel method for efficiently producing semi-dense matches across images, addressing the limitations of previous detector-free matchers like LoFTR, which suffered from low efficiency despite remarkable matching capabilities in challenging scenarios. Efficient LoFTR revisits design choices to improve both efficiency and accuracy. "We present a novel method for efficiently producing semi-dense matches across images. Previous detector-free matcher LOFTR has shown remarkable matching capability in handling large-viewpoint change and texture-poor scenarios but suffers from low efficiency. We revisit its design choices and derive multiple improvements for both efficiency and accuracy. One key observation is that performing the transformer over the entire feature map is redundant due to shared local information, therefore we propose an aggregated attention mechanism with adaptive token selection for efficiency. Furthermore, we find spatial variance exists in LoFTR's fine correlation module, which is adverse to matching accuracy. A novel two-stage correlation layer is proposed to achieve accurate subpixel correspondences for accuracy improvement. Our efficiency optimized model is ~2.5 faster than LoFTR which can even surpass state-of-the-art efficient sparse matching pipeline SuperPoint + LightGlue. Moreover, extensive experiments show that our method can achieve higher accuracy compared with competitive semi-dense matchers, with considerable efficiency benefits. This opens up exciting prospects for large-scale or latency-sensitive applications such as image retrieval and 3D reconstruction. Project page: https://zju3dv.github.io/efficientloftr/"

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matchanything_eloftr

zju-community

The MatchAnything-ELOFTR model was proposed in **"MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training"** by Xingyi He, Hao Yu, Sida Peng, Dongli Tan, Zehong Shen, Hujun Bao, and Xiaowei Zhou from Zhejiang University and Shandong University. This model is a version of **ELOFTR** enhanced by the MatchAnything pre-training framework. This framework enables the model to achieve universal cross-modality image matching capabilities, overcoming the significant challenge of matching images with drastic appearance changes due to different imaging principles (e.g., thermal vs. visible, CT vs. MRI). This is achieved by pre-training on a massive, diverse dataset synthesized with cross-modal stimulus signals, teaching the model to recognize fundamental, appearance-insensitive structures. "Image matching, which aims to identify corresponding pixel locations between images, is crucial in a wide range of scientific disciplines, aiding in image registration, fusion, and analysis. In recent years, deep learning-based image matching algorithms have dramatically outperformed humans in rapidly and accurately finding large amounts of correspondences. However, when dealing with images captured under different imaging modalities that result in significant appearance changes, the performance of these algorithms often deteriorates due to the scarcity of annotated cross-modal training data. This limitation hinders applications in various fields that rely on multiple image modalities to obtain complementary information. To address this challenge, we propose a large-scale pre-training framework that utilizes synthetic cross-modal training signals, incorporating diverse data from various sources, to train models to recognize and match fundamental structures across images. This capability is transferable to real-world, unseen cross-modality image matching tasks. Our key finding is that the matching model trained with our framework achieves remarkable generalizability across more than eight unseen cross-modality registration tasks using the same network weight, substantially outperforming existing methods, whether designed for generalization or tailored for spe

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월간 입력 토큰 10 M월간 출력 토큰 2 M1 M = 100만 토큰 · USD
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