arXiv Computer Vision
RoMa-$\Omega$: What Feed-Forward 3D Models Know About Image Matching
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arXiv Computer Vision
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Unlocking Zero-shot Potential of Semi-dense Image Matching via Gaussian Splatting
arXiv:2511.21265v2 Announce Type: replace Abstract: Learning-based image matching critically depends on large-scale, diverse, and geometrically accurate training data. 3D Gaussian Splatting (3DGS) en...
arXiv Computer Vision
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VGGT-$\Omega$
arXiv:2605.15195v2 Announce Type: replace Abstract: Recent feed-forward reconstruction models, such as VGGT, have proven competitive with traditional optimization-based reconstructors while also prov...
Hugging Face Trending Papers
Jul 20
MuViSeg: Multi-View Segment Correspondences from Dense Geometry Priors
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 Computer Vision
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Geometry-Grounded Articulation and Test-Time Adaptation for Zero-Shot, Scalable Shape Matching in the Wild
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arXiv Computer Vision
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Geometry-Aligned Semantic Matching for Cross-Modal Planar Image Registration
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.
arXiv AI
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SemGeo-Gen: Unsupervised Generation of Approximate Cross-Instance Semantic-Geometric Correspondences
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.