arXiv:2603.23305v2 Announce Type: replace-cross
Abstract: We investigate the problem of matching networks where both edge weights and node features are correlated following a specific node correspond...
By Mohammad Hassan Ahmad Yarandi, Luca Ganassali
arXiv:2609.16378v1 Announce Type: cross
Abstract: Digital twins provide a scalable and cost-effective complement to real-world testing for validating autonomous-driving and advanced driver-assistance...
By Ghazal Farhani, Taufiq Rahman
The paper introduces CELL, a confidence-weighted event-camera localization method that learns per-correspondence confidence end-to-end using a differentiable probabilistic PnP. It reweights flow supervision, drives probabilistic correspondence selection at test time, and refines edge matching, while also proposing a partial-completion depth representation. Experiments on M3ED and DSEC show significant reductions in median translation and rotation errors compared to the LEAR baseline.
By Panagiotis Kiousis, Kuangyi Chen, Jun Zhang, Friedrich Fraundorfer
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
Terrestrial Laser Scanning (TLS) point clouds captured in urban environments frequently suffer from glass-induced reflection artifacts, severely degrading downstream applications. Existing reflection artifact removal methods generally rely on ideal reflection symmetry assumptions, yet their performance is limited by inaccurate glass estimation and insufficient geometric representations.
The paper introduces a sparse topometric graph for indoor navigation that encodes local 3D geometry in nodes and distance‑weighted edges for traversable connectivity. A learned graph‑matching network aligns subgraph structures across different sessions, enabling robust point‑goal navigation even with scene changes, sensor variations, or limited overlap. Experiments on Gibson and 3D‑FRONT datasets show higher success rates and a memory reduction of over two orders of magnitude compared to point‑level maps.
By Eun Sun Lee, Minkyun Seo, Gwangtak Bae, Junho Kim, Seunggu Kang, Jaesik Park, Young Min Kim