arXiv Computer Vision
TurboVGGT: Fast Visual Geometry Reconstruction with Adaptive Alternating Attention
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arXiv Computer Vision
Sep 22
VGGT-Prime: Compute-Adaptive Mixture-of-Heads for Efficient Visual Geometry Transformers
arXiv:2609.23733v1 Announce Type: new Abstract: Feed-forward visual geometry models such as the Visual Geometry Grounded Transformer (VGGT) have recently enabled direct 3D reconstruction from multi-v...
arXiv Machine Learning
Jul 7
SAF3R: Dynamic Sparse Attention for Feed-Forward 3D Reconstruction Transformers
arXiv:2607. 03612v1 Announce Type: cross Abstract: Feed-forward 3D reconstruction (F3R) transformers have recently achieved remarkable success.
arXiv AI
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LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows
arXiv:2604. 05182v2 Announce Type: replace-cross Abstract: We introduce the Large Sparse Reconstruction Model to study how scaling transformer context windows affects feed-forward 3D reconstruction.
arXiv AI
Sep 10
UniQueR: Unified Query-based Feedforward 3D Reconstruction
UniQueR is a unified query‑based feedforward framework that reconstructs 3D scenes from unposed images by treating reconstruction as a sparse 3D query inference problem. It learns a compact set of 3D anchor points that serve as explicit geometric queries, allowing the network to infer scene structure—including occluded geometry—in a single forward pass. By encoding spatial and appearance priors directly in global 3D space and using a decoupled cross‑attention design, UniQueR achieves strong geometric expressiveness while reducing memory and computational cost, outperforming state‑of‑the‑art feedforward methods on Mip‑NeRF 360 and VR‑NeRF with far fewer primitives.
Hugging Face Trending Papers
Jul 2
Diversity-aware View Partitioning for Scalable VGGT
Geometry transformers such as VGGT achieve strong performance by jointly reasoning over multiple views with global attention. However, scaling them to large view collections remains challenging due to the quadratic cost of attention.
arXiv Computer Vision
Sep 21
VGGT-CAD: Reconstructing Parametric CAD 3D Model with Geometric Grounding
VGGT-CAD is a geometry‑aware framework that reconstructs parametric CAD 3D models from single and multi‑view images. It incorporates pretrained 3D geometric priors by encoding camera parameters as condition tokens and jointly modeling them with image tokens. The method introduces a variable‑view cross‑view context aggregation module and a training‑free geometry‑aware view selection strategy, and decodes the learned representation into CAD command sequences using a non‑autoregressive decoder. Additionally, VideoCAD, a large‑scale multi‑view video benchmark derived from existing CAD data, is presented to evaluate the approach.