FlashVGGT: Efficient and Scalable Visual Geometry Transformers with Compressed Descriptor Attention
3D reconstruction from multi-view images is a core challenge in computer vision. Recently, feed-forward methods have emerged as efficient and robust alternatives to traditional per-scene optimization techniques. Among them, state-of-the-art models like the Visual Geometry Grounding Transformer (VGGT) leverage full self-attention over all image tokens to capture global relationships. However, this approach suffers from poor scalability due to the quadratic complexity of self-attention and the large number of tokens generated in long image sequences. In this work, we introduce FlashVGGT, an efficient alternative that addresses this bottleneck through a descriptor-based attention mechanism. Instead of applying dense global attention across all tokens, FlashVGGT compresses spatial information from each frame into a compact set of descriptor tokens. Global attention is then computed as cross-attention between the full set of image tokens and this smaller descriptor set, significantly reducing computational overhead. Moreover, the compactness of the descriptors enables online inference over long sequences via a chunk-recursive mechanism that reuses cached descriptors from previous chunks. Experimental results show that FlashVGGT achieves reconstruction accuracy competitive with VGGT while reducing inference time to just 9.3% of VGGT for 1,000 images, and scaling efficiently to sequences exceeding 3,000 images. Our project page is available at https://wzpscott.github.io/flashvggt_page/.
69305d972d1e5b0a7d84d836
Zipeng Wang
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2025-12-04T08:47:52.505Z
65d31df8e3667040af7bc945
/avatars/abe1137fccc45da0e1bd41ef0c172899.svg
Zipeng Wang
ZipW
user
ZipW
69305d972d1e5b0a7d84d837
Dan Xu
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2025-12-04T08:47:50.694Z
66feab48651e00e22f33222e
/avatars/7344377e2c796c7ec85194bb2fc78521.svg
Dan Xu
danxuhk
user
danxuhk
FlashVGGT: Efficient and Scalable Visual Geometry Transformers with Compressed Descriptor Attention
3D reconstruction from multi-view images is a core challenge in computer vision. Recently, feed-forward methods have emerged as efficient and robust alternatives to traditional per-scene optimization techniques. Among them, state-of-the-art models like the Visual Geometry Grounding Transformer (VGGT) leverage full self-attention over all image tokens to capture global relationships. However, this approach suffers from poor scalability due to the quadratic complexity of self-attention and the large number of tokens generated in long image sequences. In this work, we introduce FlashVGGT, an efficient alternative that addresses this bottleneck through a descriptor-based attention mechanism. Instead of applying dense global attention across all tokens, FlashVGGT compresses spatial information from each frame into a compact set of descriptor tokens. Global attention is then computed as cross-attention between the full set of image tokens and this smaller descriptor set, significantly reducing computational overhead. Moreover, the compactness of the descriptors enables online inference over long sequences via a chunk-recursive mechanism that reuses cached descriptors from previous chunks. Experimental results show that FlashVGGT achieves reconstruction accuracy competitive with VGGT while reducing inference time to just 9.3% of VGGT for 1,000 images, and scaling efficiently to sequences exceeding 3,000 images. Our project page is available at https://wzpscott.github.io/flashvggt_page/.
FlashVGGT uses descriptor-based attention to efficiently perform 3D reconstruction from multi-view images, significantly reducing inference time and improving scalability compared to VGGT.
feed-forward methods Visual Geometry Grounding Transformer (VGGT) self-attention descriptor-based attention descriptor tokens cross-attention online inference chunk-recursive mechanism