arXiv:2608.03423v2 Announce Type: replace
Abstract: Local feature matching is a fundamental component of photogrammetry, enabling accurate image correspondence critical for tasks such as 3D reconstru...
By Zhihua Xu, Runyu Zhu, Rongjun Qin
arXiv:2608.21710v1 Announce Type: new
Abstract: With the advance of deep neural networks, the quality of disparity maps obtained through stereo matching has steadily improved. However, existing stere...
By Bohan Li
arXiv:2609.39748v1 Announce Type: new
Abstract: Scaling has become a primary driver of progress in language and vision foundation models, yet its role in precise correspondence matching remains under...
By Yongjian Zhang, Longguang Wang, Zhuo Song, Zhiheng Fu, Liang Lin, Yulan Guo
arXiv:2608.29819v1 Announce Type: new
Abstract: Accurate stereo matching remains challenging in ill-posed regions such as fine structures, reflective, or transparent objects, where appearance cues ar...
By Md Raqib Khan, Santosh Kumar Vipparthi, Subrahmanyam Murala
The paper introduces S2A, a semantic-to-spatial alignment framework designed for alignment‑free RGB‑T salient object detection. It employs a global‑guided hierarchical fusion module to refine intra‑modal features, an alignment‑free cross‑modal channel attention module to exchange semantic information, and a spatial deformable cross‑attention module to recover local spatial correspondence. These components collectively reduce misalignment‑induced feature contamination and achieve competitive performance on public benchmarks without additional bells and whistles.
By Qiangqiang Zhou, Yang Luo, Yong Chen, Jiawei Xu
arXiv:2607. 03612v1 Announce Type: cross Abstract: Feed-forward 3D reconstruction (F3R) transformers have recently achieved remarkable success.
By Jianing Deng, Yuanzhe Li, Jialu Wang, Song Wang, Tianlong Chen, Huanrui Yang, Jingtong Hu