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arXiv Computer Vision

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.

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arXiv Computer Vision
1d ago

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
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arXiv Machine Learning
Sep 23

SAM-V: Geometry-Aware Segment Anything for Multi-View Instance Segmentation

SAM‑V is a geometry‑aware extension of the Segment Anything Model (SAM) that integrates 3D priors from a feed‑forward geometry model (VGGT) into 2D segmentation. It uses a prompt‑fusion mechanism to combine sparse SAM prompts with view‑specific camera tokens and local VGGT features, enabling a mask decoder that attends to both dense 2D and 3D cues. The resulting end‑to‑end system produces consistent multi‑view instance segmentation in a single forward pass, achieving significant gains on the IGGT 3D tracking benchmark without offline mask matching or explicit 3D reconstruction.

By Jiangshan Gong, Yuqun Wu, Qiqian Fu, Yao Xiao, Chuhang Zou, Shenlong Wang, Derek Hoiem
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arXiv AI
Sep 21

Think Locally, Refine Globally for Memory-Efficient 3D Reconstruction

LoG-VGGT is a memory‑efficient framework for long‑sequence 3D reconstruction that balances local temporal modeling with global camera consistency. It uses cross‑window attention in a small subset of transformer blocks to propagate information across adjacent temporal windows while keeping memory usage bounded. A global camera consistency refinement module further improves long‑horizon pose stability by enforcing scene‑level constraints through cross‑attention between camera and compact register tokens, leading to better depth accuracy and robust camera pose estimation on multiple benchmarks.

By Jingke Zhou, Chenhang Ma, Zhizhou Zhong, Mingkai Liu, Zhuang Zhou, Yicheng ji, Binghua Su, Bo Cai, Xianliang Huang
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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.

By Weitian Wang, Shubham Rai, Cecilia De La Parra, Akash Kumar
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arXiv Machine Learning
Oct 12

Zebra3D: Efficient Hybrid Foundation Models For Scalable 3D Reconstruction

Zebra3D introduces a layerwise sparse‑linear hybrid attention framework that enhances the efficiency of pretrained 3D foundation models without requiring training from scratch. By assigning linear‑centered attention to tolerant early stages and tile‑sparse attention to sensitive middle‑to‑late stages, it preserves context through landmark summaries and enables training‑free conversion via closed‑form affine fitting. Across multiple backbones, Zebra3D achieves strong reconstruction and camera‑pose accuracy with high attention sparsity, delivering up to 7.1× speedups at 1,024 views and maintaining performance close to dense counterparts after optional post‑conversion training.

By Utkarsh Saxena, Ruijing Yang, Mehdi Rezagholizadeh, Akash Haridas, Vikram Appia, Emad Barsoum
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