SAF3R: Dynamic Sparse Attention for Feed-Forward 3D Reconstruction Transformers
For researchers working on 3D reconstruction from image sequences, this work provides an efficient attention mechanism that reduces computational cost without sacrificing performance.
The paper addresses the quadratic complexity bottleneck of cross-view global attention in feed-forward 3D reconstruction transformers when scaling to long image sequences. It proposes SAF3R, a training-free dynamic sparse attention framework that achieves high sparsity ratios while preserving reconstruction quality, yielding substantial end-to-end speedup.
Feed-forward 3D reconstruction (F3R) transformers have recently achieved remarkable success. However, scaling them to long image sequences remains challenging, as the quadratic complexity of cross-view global attention quickly becomes the dominant computational bottleneck. While recent efforts attempt to improve efficiency through compressed or sparse attention, they fail to fully exploit the inherent sparsity and dynamic behavior of global attention. In this work, we present a comprehensive analysis of global attention across multiple F3R transformers and reveal that attention patterns are highly heterogeneous, dynamic, and extremely sparse across layers and attention heads. Motivated by these findings, we propose SAF3R, a training-free dynamic sparse attention framework tailored to F3R transformers. SAF3R integrates tailored sparse attention mechanisms with offline head profiling and an efficient online adaptation strategy to match input-dependent attention behaviors. Extensive experiments demonstrate that SAF3R achieves high sparsity ratios while preserving camera pose estimation and 3D reconstruction quality, translating into substantial end-to-end speedup on F3R transformers compared to existing methods. Code is available at https://github.com/jndeng/SAF3R