arXiv Computer Vision
VGGT-Prime: Compute-Adaptive Mixture-of-Heads for Efficient Visual Geometry Transformers
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arXiv Computer Vision
Oct 28
GPA-VGGT: Geometry-Preserving Acceleration of VGGT
GPA‑VGGT is a training‑free acceleration technique for the VGGT feed‑forward geometry model. It replaces VGGT’s global attention in early blocks with frame‑wise attention that uses a shared cross‑frame key/value set, and in later blocks it merges only keys and values while preserving queries and restoring attention mass. The method achieves speedups comparable to existing efficiency approaches while maintaining over 99% of VGGT’s pose accuracy on multi‑view geometry tasks.
arXiv Computer Vision
Oct 15
TurboVGGT: Fast Visual Geometry Reconstruction with Adaptive Alternating Attention
arXiv:2605.14315v2 Announce Type: replace Abstract: Recent feed-forward 3D reconstruction methods, such as visual geometry transformers, have substantially advanced the traditional per-scene optimiza...
arXiv Computer Vision
Oct 22
BlazeVGGT: 13x Faster VGGT at 2,000 Frames via Camera Token without Training
BlazeVGGT is a training‑free, plug‑and‑play module that speeds up the VGGT visual geometry model by sparsifying cross‑view attention. It selects a compact set of complementary views using a greedy determinantal point process, preserving dense per‑frame predictions while eliminating redundant computations. On 2,000‑frame sequences, BlazeVGGT achieves roughly 13× faster inference and improves Chamfer Distance from 0.41 to 0.32, demonstrating a strong accuracy–efficiency trade‑off for point‑cloud reconstruction and camera‑pose estimation.
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...
arXiv Machine Learning
Oct 8
Hardware-aware Calibrated Clustered Attention for Efficient Visual Geometric Transformers
The paper introduces Hardware‑aware Calibrated Clustered Attention (BC attention) to speed up the global attention layers of the Visual Geometry Grounded Transformer (VGGT) used for 3D scene reconstruction. By clustering queries within hardware‑friendly neighborhood blocks and adding hashing hyperplane calibration plus threshold‑based error compensation, BC attention reduces computation and memory traffic, enabling it to handle long sequences with practical latency gains on GPUs. Experiments show that calibrated BC attention accelerates global attention by 2.10–2.63× and the entire backbone by 1.77–2.35×, with only a 1% loss in accuracy, and achieves up to 2.87× latency improvement with less than 5% performance drop.
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.