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 AI
Oct 26

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.

By Roy Amoyal, Shira Ifergane, Oren Freifeld
arXiv Computer Vision
Oct 2

Dyna3: VLM-Guided Training-Free 4D Reconstruction via Depth Foundation Models

Dyna3 is a training‑free framework that extends the depth foundation model DA3 to perform 4D dynamic scene reconstruction without fine‑tuning. By leveraging DA3’s cross‑view features and a best‑match search, it distinguishes static surfaces from moving objects, and uses vision‑language models to generate semantic prompts for SAM 3 to achieve precise instance‑level segmentation. Experiments on four datasets show Dyna3 outperforms correspondence‑trained methods, improving dynamic object segmentation by +5.5 pp, speeding pose estimation 13×, and reducing memory usage 4–8×.

By Xinhao Xiang, Weiyang Li, Zhijie Zheng, Abhijeet Rastogi, Jiawei Zhang
arXiv Computer Vision
Oct 27

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...

By Jianyuan Wang, Minghao Chen, Shangzhan Zhang, Nikita Karaev, Johannes Sch\"onberger, Patrick Labatut, Piotr Bojanowski, David Novotny, Andrea Vedaldi, Christian Rupprecht