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arXiv Computer Vision

VGGT-Bridge: Beyond Sequential Pose Graphs via Coarse-Stride Skip Edges

VGGT-Bridge extends feed‑forward visual geometry transformers by adding long‑range skip edges between non‑adjacent chunks, reducing drift in multi‑view 3D reconstruction. It processes sparsely sampled coarse chunks to bridge distant fine chunks, corrects first‑frame scale bias with reverse‑direction sampling, and incorporates a loop‑aware policy for compatibility with loop closures. The method achieves up to 28.3% absolute trajectory error reduction on KITTI, 18.8% on Virtual KITTI, and 10.0% on Waymo Open compared to the SwiftVGGT baseline.

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arXiv Computer Vision
5d ago

HiP3R: Hierarchical Pose Graphs for Long-Horizon 3D Reconstruction

HiP3R introduces a hierarchical pose graph approach for long‑horizon 3D reconstruction, addressing the trade‑off between chunk‑level and frame‑level optimization. It first builds a robust chunk‑level Sim(3) graph to set the global layout and scale, then fixes this layout while estimating a single SE(3) camera pose per frame to merge overlapping predictions into a unified trajectory. Experiments on multiple benchmarks show that HiP3R improves camera trajectories and 3D reconstruction quality for both chunk‑based and streaming backends without extra training.

By Zunnan Xu, Shangchen Zhou, Yihang Luo, Chen Change Loy
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arXiv Computer Vision
6d ago

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.

By Shujun Yao, Junhui Lin, Congying Han, Tiande Guo, Ziwen Liu, Bonan Li
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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 Computer Vision
Sep 25

MDE-VIO: Enhancing Visual-Inertial Odometry Using Learned Depth Priors

MDE-VIO integrates learned depth priors into the VINS-Mono optimization backend to improve visual‑inertial odometry in low‑texture environments. The framework enforces affine‑invariant depth consistency and pairwise ordinal constraints while filtering unstable artifacts with variance‑based gating, keeping computation within edge‑device limits. Experiments on TartanGround and M3ED datasets show the method prevents divergence and reduces Absolute Trajectory Error by up to 28.3%.

By Arda Alniak, Sinan Kalkan, Mustafa Mert Ankarali, Afsar Saranli, Abdullah Aydin Alatan
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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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