Visual Geometry Grounded Transformer (VGGT) recovers dense 3D scene structure from multi-view images in one forward pass, but quadratic cross-frame attention limits its scalability. Existing training-free accelerators reduce computation uniformly along one axis, missing layer heterogeneity.
arXiv:2607. 03612v1 Announce Type: cross Abstract: Feed-forward 3D reconstruction (F3R) transformers have recently achieved remarkable success.
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: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: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...
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.
arXiv:2605.23889v2 Announce Type: replace Abstract: Online 3D reconstruction requires estimating camera pose and scene geometry under strict causal and bounded-memory constraints. Existing methods of...
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:2610.01013v1 Announce Type: new Abstract: Feed-forward 3D vision models such as VGGT have achieved remarkable progress, unifying camera estimation and dense scene reconstruction in a single pas...
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.
TALON introduces Token‑Aligned Lightweight Adapters for 6‑DoF spacecraft pose estimation, inserting spatiotemporal 3D adapters before self‑attention layers of a frozen Vision Transformer. A patch‑token alignment loss grounds adapted features to keypoint structure, enabling the frozen backbone to reason over temporally enriched tokens with minimal added parameters. On the SPADES and SwissCube datasets, TALON cuts pose error by 50% and improves ADD‑0.1d accuracy by 21.8%, respectively, while also achieving significant gains in zero‑shot sim‑to‑real cross‑domain evaluation.
Stream4D is a training‑free, motion‑decoupled streaming 4D reconstruction framework that can be integrated into causal Transformer backbones such as StreamVGGT and Stream3R. It exploits the causal attention’s KV cache to aggregate temporal motion cues, fuses them with depth priors, and produces precise dynamic masks that separate static and dynamic regions. A soft key masking scheme then suppresses dynamic interference in attention, improving camera pose estimation and reducing ghosting in long sequences.