SUFLECA is a weakly supervised framework that improves zero‑shot CAD‑to‑image alignment by scaling geometry‑grounded feature learning using Normalized Object Coordinates across up to 12 real and synthetic datasets. It introduces a geometrically consistent matching algorithm that reliably establishes CAD‑to‑image correspondences, enabling accurate, sub‑second alignment without iterative pose refinement. On the ScanNet25k benchmark, SUFLECA achieves 32.8%/42.6% category/instance accuracy, outperforming the strongest zero‑shot baseline by 9.7/12.5 percentage points and surpassing existing pose‑supervised methods for the first time.
By Saad Ejaz, Miguel Fernandez-Cortizas, Javier Civera, Holger Voos, Jose Luis Sanchez-Lopez
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
arXiv:2608. 19693v1 Announce Type: cross Abstract: Sparse keypoint extraction and matching underpin core tasks in geometric computer vision, including structure-from-motion, visual SLAM, augmented reality, and medical image registration.
By Johannes K\"unzel, Peter Eisert, Anna Hilsmann
arXiv:2607. 13449v1 Announce Type: cross Abstract: 6-DoF pose estimation is a critical task in autonomous rendezvous and proximity operations.
By Josiane Uwumukiza, Jocelyn Zhao, Giovanni Lavezzi, Giacomo Battaglia, Paolo Panicucci, Minduli C. Wijayatunga, Victor Rodriguez-Fernandez, Richard Linares
arXiv:2610.01098v1 Announce Type: new
Abstract: Illusory matches between distinct yet visually similar 3D surfaces--doppelgangers--remain a fundamental obstacle for large-scale, in-the-wild 3D recons...
By Hanyuan Xiao, Gonglin Chen, Haolin Xiong, Wenbin Teng, Haiwei Chen, Yajie Zhao
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.
By Haodi Yao, Fenghua He, Ning Hao, Yao Su
SemGeo-Gen is an unsupervised approach that generates approximate cross‑instance semantic‑geometric correspondences from weakly aligned 3D object collections. It uses coarse category‑level rotation alignment, lifts multi‑view DINOv2 features to 3D, applies continuous piecewise‑affine registration, and prunes matches with semantic consistency to produce 3D correspondences that can be projected into 2D views for scalable supervision. The method achieves high accuracy against human annotations and improves state‑of‑the‑art semantic correspondence models after synthetic pretraining.
By Roy Amoyal, Shira Ifergane, Oren Freifeld
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
The paper introduces a new approach to 3D object pose estimation that leverages diffusion model features, such as those from Stable Diffusion, to improve performance on unseen objects. Three aggregation architectures—vanilla, nonlinear, and context‑aware—are proposed, with a confidence‑adaptive aggregation network that selectively uses discriminative features to balance speed and accuracy. The method achieves state‑of‑the‑art accuracy on unseen object benchmarks (97.7% vs. 93.5% on Unseen LM) and real‑time performance (68 FPS) while reducing computational cost.
By Tianfu Wang, Guosheng Hu, Hongguang Wang
arXiv:2608.22054v1 Announce Type: new
Abstract: Structure-from-Motion (SfM) aims to estimate camera poses and reconstruct 3D structures from a collection of unordered images. Compared with incrementa...
By Jiamin Xu, Lixing Yao, Weichen Dai, Renshu Gu, Zunjie Zhu, Weiwei Xu, Gang Xu
The paper introduces a benchmark for evaluating Structure-from-Motion (SfM) methods used in novel view synthesis (NVS) such as NeRFs and 3DGS. It compares classical SfM, which relies on local feature matches and bundle adjustment, with recent neural network approaches that regress camera parameters directly for faster but less accurate results. The study finds that reducing feature counts speeds up classical SfM without sacrificing pose accuracy, and that combining neural network initial estimates with classical refinement yields the best balance of efficiency and effectiveness.
By Jhacson Meza, Martin R. Oswald, Torsten Sattler
Geometry-Aligned Semantic Matching for Cross-Modal Planar Image Registration proposes CDPM, a method that first aligns semantic representations across modalities and then refines correspondences with fine-grained CNN features. CDPM adapts DINOv3 using geometrically consistent cross-modal patch pairs, builds a DINO-Centric Feature Pyramid for stable cross-modal matching, and adds a lightweight CNN branch for precise local refinement. Experiments on three cross-modal datasets show that CDPM outperforms existing dense matchers, improving AUC metrics and reducing mACE while using fewer FLOPs.
By Zhiwei Wang, Defeng He, Yuxing Li, Meilu Zhu, Edmund Y. Lam