We present a reproducibility study of XFeat, a lightweight local feature extractor and matcher designed to identify corresponding points across images efficiently on resource-constrained hardware. We re-implement the architecture based on the paper and supplementary material, re-evaluate the authors' released checkpoint alongside our re-implementation, and conduct additional architectural ablations to examine design choices that were not fully justified in the original work.
arXiv:2609.37013v1 Announce Type: cross
Abstract: Rapid assessment of building damage after natural disasters is essential to support emergency response. Earth Observation satellites can acquire rele...
By Thomas Goudemant, Benjamin Francesconi, Marjorie Bellizzi, Adrien Dorise
arXiv:2606.29167v2 Announce Type: replace
Abstract: Dense correspondence on in-the-wild 3D scans must handle severe non-isometric deformation, partial observations, topology artifacts, irregular disc...
By Qilong Liu, Qinfeng Xiao, Chenyuan Yi, Yongsheng Lin, Liying Zhang, Kit-lun Yick
arXiv:2606.03406v2 Announce Type: replace
Abstract: Reliable correspondence estimation supports image processing and 3D vision tasks, including Structure from Motion, visual localization, and image r...
By Xu Pan, Zhen Pang, Qiyuan Ma, Wei Ji, Shuhan Shen, Xianwei Zheng
arXiv:2608. 16658v1 Announce Type: cross Abstract: Cross-view Video Geo-localization (CVG) aims to localize ground-view videos by retrieving their corresponding geo-tagged aerial images.
By Zichao Zeng, Weijia Fan, Yufan Chen, June Moh Goo, Junwei Zheng, Ruiping Liu, Kunyu Peng, Jiaming Zhang, Rainer Stiefelhagen, Jan Boehm
Classical image correspondence is solved at the level of sparse keypoints or dense pixels, but the systems that consume these matches - object-level mapping, topological navigation, scene-graph maintenance - reason about whole objects. Recent work narrows this gap by matchng directly at the level of instance segments: a class-agnostic segmenter partitions each image, and per-segment descriptors are obtained by pooling features from large 3D foundation models over the masks.
arXiv:2608.22300v1 Announce Type: new
Abstract: Co-registration underlies nearly every multi-temporal and multi-sensor use of optical satellite imagery, and operational products still carry documente...
By Shoukun Sun, Zhe Wang, Sanaz Salati, Jiyin Zhang, Hui Wang, Xiaogang Ma
The paper introduces TFA, a training‑free aggregation technique that calibrates frozen visual foundation models for visual place recognition. TFA uses cross‑codebook agreement, retrieval coverage, and spectral statistics to adjust residual assignment, spectral shaping, and global‑feature fusion without requiring place labels or task‑specific weights. Experiments with a DINOv2‑B backbone show significant Recall@1 gains over existing training‑free methods across multiple benchmarks, demonstrating that reliability‑guided aggregation can unlock additional retrieval performance from frozen representations.
By Xin Li, Zhimin Mao, Shang Wang, Siyuan Duan, Geng Zhang
arXiv:2608.29733v1 Announce Type: new
Abstract: Visual aliasing, also known as the doppelganger problem, remains a key challenge for structure-from-motion (SfM): visually similar but physically disti...
By Gonglin Chen, Ben Southall, Hanyuan Xiao, Wenbin Teng, Haolin Xiong, Tianwen Fu, Junyi Ouyang, Kshitij Singh Minhas, Supun Samarasekera, Rakesh Kumar, Yajie Zhao
ARC‑Loc introduces a new cross‑view localization method that bypasses heavy Bird’s‑Eye‑View transformations and external depth models. By converting ground keypoints into azimuthal rays on a satellite map and exploiting their convergence at the user’s location, the approach uses a minimal Azimuthal Ray Convergence solver and an ARC loss to directly match ground and satellite images. Experiments on VIGOR and KITTI show that ARC‑Loc achieves competitive accuracy while offering faster, memory‑efficient inference and easy integration with existing frameworks.
By Hyeongsik Kim, Mincheol Kim, Heejoon Moon, Je Hyeong Hong
GeoStore is a new benchmark for fine‑grained point‑of‑interest (POI) localization that matches close‑up storefront photos against large geo‑tagged street‑view images, a task distinct from traditional visual place recognition. The paper shows that global‑descriptor methods designed for symmetric matching perform poorly on this asymmetric problem, and introduces GLAM, a Global‑to‑Local Asymmetric Matching approach that combines a global retrieval anchor with a lightweight local re‑ranking using pooled region tokens. GLAM achieves higher Recall@1/5/10 and mAP than strong baselines while using far fewer re‑ranking features and significantly lower per‑pair matching cost.
By Lu Han, Xiting Sun, Hao Wang, Zhiqiang Cao, Ruihuan Du, Ziquan Zeng, Chunlong Lv
HGSQ is a real‑time aerial small‑object detector that uses a heatmap‑guided sparse query strategy to focus computation on foreground regions. It introduces a lightweight Heatmap Budget Predictor to generate a foreground budget map, and then employs Heatmap‑Guided Sparse Query Selection, Heatmap‑Gated Lite Snake Convolution, and Adaptive Query‑Decoder Budgeting to efficiently process only small‑object areas. On NWPU VHR‑10 and VisDrone2019, HGSQ achieves 95.10 mAP50 and 54.8 mAP50 respectively while running at 96 FPS with only 48.6 GFLOPs on an RTX 4070.
By Yangchen Zeng