TokenMatch is a transformer-based model that estimates 3D shape correspondences by adaptively tokenising meshes into curvature-guided patches. Trained only on the BeCoS partial-to-partial dataset, it generalises to full-shape matching without retraining, using self‑ and cross‑attention to learn patch‑ and point‑level relations. Evaluated on CP2P, PSMAL, BeCoS, FAUST, SCAPE, and SHREC'19, TokenMatch consistently outperforms existing methods in mean geodesic error and intersection‑over‑union while achieving sub‑second inference speeds.
By Adeela Islam, Zorah L\"ahner, Vittorio Murino, Vladislav Golyanik
The paper introduces SADGE, a quantitative similarity metric that predicts how well synthetic image datasets will perform on real-world computer vision tasks without requiring downstream model training. SADGE combines appearance and geometric similarity measures through a constrained bilinear interaction, outperforming appearance-only and geometry-only baselines across 79,000 image pairs in five synthetic-to-real benchmark families. The study demonstrates that the non‑linear interplay between appearance and geometry, rather than either alone, best predicts downstream performance in tasks such as object detection, semantic segmentation, and pose estimation.
By Patryk Bartkowiak, Dominik Michels, Soren Pirk, Wojtek Palubicki
CALIPER is a model‑free RGB‑D framework that performs fine‑grained recognition of visually similar industrial parts by combining support‑based appearance matching with metric size evidence. Each class is onboarded from a single turntable RGB‑D video and a few labeled real images, enabling 3D reconstruction for appearance support and depth‑aligned size profiling. At inference, a YOLOv8n‑seg model localizes parts, a frozen DINOv2 backbone with an episodically trained embedding head matches support, and margin‑conditioned metric fusion selectively uses size evidence for ambiguous cases, achieving high accuracy on 18 parts and robust enrollment of unseen screws without retraining.
By Alankrit Gupta, Chenxi Tao, Seung-Kyum Choi
arXiv:2606.10142v2 Announce Type: replace
Abstract: Recent advances in 3D generation have led to substantial improvements in realism, controllability, and efficiency, yet the evaluation of 3D assets...
By Nanshan Jia, Zhenyu Zhao, Junbo Peng, Sui Huang, Jingshen Wang, Zeyu Zheng
The study investigates how vision models represent objects compared to humans during physical reasoning tasks. By comparing segmentation outputs from several architectures (DINOv2, SegFormer, DeepLabV3+, UPerNet) to human behavior in time‑to‑collision and change‑detection tasks, the authors find that briefly trained models are too coarse, fully trained models are too fine, and an intermediate training stage best matches the coarse, volumetric bodies humans use. Larger models reach this intermediate alignment earlier, suggesting that resource constraints drive the emergence of human‑like representations in general‑purpose vision systems.
By Andrey Gizdov, Andrea Procopio, Lorenzo Caputi, Georgi I. Ivanov, Yichen Li, Daniel Harari, Tomer Ullman
arXiv:2604. 07282v2 Announce Type: replace-cross Abstract: Automated face recognition has made rapid strides over the past decade due to the unprecedented rise of deep neural network (DNN) models that can be trained for domain-specific tasks.
By Fizza Rubab, Yiying Tong, Arun Ross