arXiv:2606. 07907v1 Announce Type: cross Abstract: In our previous work, a deep learning-based framework for 3D intraoral reconstruction was proposed.
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arXiv:2606. 05998v1 Announce Type: cross Abstract: Oral 3D modelling is one of the most essential stages in dentistry, and many different approaches, such as impression taking and intraoral scanning, are commonly used for this phase, each with notable limitations.
arXiv:2511. 16624v2 Announce Type: replace-cross Abstract: We present SAM 3D, a generative model for visually grounded 3D object reconstruction, predicting geometry, texture, and layout from a single image.
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...
RecGen3D is a framework that merges feed‑forward reconstruction and diffusion‑based generation to address the trade‑off between reconstruction fidelity and generative plausibility in sparse‑view 3D modeling. By aligning both models in a shared canonical space and using decoupled cooperative learning, the system stabilizes training and allows the reconstruction module to supply canonical geometric anchors while the diffusion generator refines and completes the structure. Experiments show that RecGen3D outperforms existing methods in producing complete and consistent 3D models from sparse observations.
SAMV-DUSt3R is an end‑to‑end model that injects SAM2 2D masks into MV‑DUSt3R reconstruction to decouple objects from 3D scenes. A Cross Flow Mask Block steers the network toward the target instance, improving shape accuracy and achieving object‑level disentanglement without multi‑stage pipelines. A lightweight Spatial RankGNN selects the optimal reference view with 73.5% accuracy, and experiments show an 11% boost in average reconstruction precision over state‑of‑the‑art baselines.
The paper introduces Ex‑Sim(3)‑Reg, a fast and robust method for pruning 2D‑3D correspondences by reformulating the problem as an extended Sim(3) registration that explicitly accounts for depth noise. The authors provide a theoretical justification and demonstrate that their approach improves registration recall by up to 24.7% on several benchmark datasets, outperforming state‑of‑the‑art baselines. The code for the method is publicly available on GitHub.