MVLoMa is a sparse star‑graph matcher that extends the LoMa local‑feature matching framework from image pairs to multi‑view settings. It uses a frozen multi‑view geometry model to jointly describe all images in a star, injecting cross‑view geometric context into local descriptors and performing centre‑to‑neighbour matching in a single forward pass with per‑view self‑attention and bidirectional centre‑neighbour cross‑attention. On 25‑view star graphs from HyperSim, ScanNet++, Mip‑NeRF 360, and Tanks and Temples, MVLoMa achieves the best macro‑average relative‑pose AUC at all thresholds and is 2.20 times faster than sequential pairwise evaluation, while supporting multiple frozen geometry encoders and demonstrating the benefits of geometry‑aware descriptors, visibility filtering, geometric negative supervision, and joint multi‑view inference.
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: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:2607.02486v2 Announce Type: replace
Abstract: Descriptor-free visual localization eliminates high-dimensional descriptor storage, preserves scene privacy, and simplifies map maintenance, yet it...
By Yejun Zhang, Xinjue Wang, Zihan Wang, Esa Rahtu, Juho Kannala
The paper introduces Self-Geometry, a plug‑and‑play test‑time adaptation framework that enforces explicit multi‑view geometric constraints on Vision Foundation Models (VFMs) using 2D pixel correspondences as pseudo ground truth. It combines Geometric Disentanglement Optimization—mixing Multi‑View and Epipolar Consistency losses with Gradient Disentanglement—to avoid gradient conflicts, a Frame Angular‑Neighbor sampler based on SO(3) geodesic distances to select informative views, and a Lightweight TTA module that adapts VFMs via LoRA. Experiments on six VFMs and four benchmarks (7Scenes, ETH3D, ScanNet++, HiRoom) show consistent improvements in pose and geometry estimation.
By Seokhyun Youn, Dahyeon Kye, Sung-Ho Bae, Jihyong Oh
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
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
arXiv:2606.16569v2 Announce Type: replace
Abstract: Registering two captures of the same indoor space taken at different times underpins persistent spatial memory for robots and AR systems, yet the r...
By Zhiang Chen, Nahyuk Lee, Boyang Sun, Taein Kwon, Marc Pollefeys, Zuria Bauer, Sunghwan Hong
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:2609.09507v1 Announce Type: new
Abstract: Learned image matching has experienced significant progress in recent years, culminating in robust and accurate matchers such as RoMa, whose robustness...
By David Nordstr\"om, Xinyue Zhang, Thibaut Loiseau, Vincent Lepetit, Fredrik Kahl
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