arXiv Computer Vision
2d ago

SCCM: Spherically Consistent Coarse Matching for ERP Dense Feature Correspondence

SCCM (Spherically Consistent Coarse Matching) improves dense feature correspondence on equirectangular projection (ERP) imagery by correcting topological, metric, and area distortions at the coarse matching stage. It augments a chart‑naive cross‑attention matcher with spherical positional attention and area‑aware covisibility priors, raising PCK@1° from 0.229 to 0.275 on Matterport3D while keeping the refiner unchanged. In the RoMa V1 framework, SCCM outperforms ERP‑native EDM and an ERP‑retrained RoMa V1, and it transfers zero‑shot to Stanford2D3D and outdoor Holo360D.

By Gyeonggwan Lee, Eunsoo Im, Seunghwan Hong, Junghun Suh
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.

Hugging Face Trending Papers
Jul 27

LCMamNet: A Lightweight Cross-scale Mamba Network for Infrared Small Target Detection

Infrared small target detection (IRSTD) is important for low-altitude perception, unmanned-system warning, and security monitoring. However, weak targets in infrared imagery usually occupy only a few pixels and are easily submerged by cloud clutter, ground edges, and bright noise, making it difficult for lightweight segmentation-based methods to preserve local target structures while suppressing background interference.