Core Viewpoint - The article introduces FastVGGT, a training-free acceleration method that optimizes the VGGT model by addressing the redundancy in global attention mechanisms, achieving up to 4 times faster inference while maintaining reconstruction accuracy and mitigating cumulative error issues in 3D visual tasks [26]. Group 1: Main Contributions - FastVGGT enables VGGT to process 1000 input images in a single forward pass on a single GPU with 80GB VRAM, an improvement from 300 images previously [5]. - The method achieves a 4× speedup in inference time for 1000 image tasks while effectively reducing cumulative error [5][18]. - FastVGGT maintains high reconstruction quality, with improvements in metrics such as Chamfer Distance (CD) from 0.471 to 0.425 [18]. Group 2: Bottleneck Analysis - The analysis identifies that the global attention mechanism in VGGT has significant redundancy, leading to unnecessary computations [6][7]. - Cumulative error is exacerbated in long sequences due to the global attention mechanism, which amplifies minor errors over time [6]. Group 3: Methodology - Token merging strategies are introduced to optimize the redundancy in VGGT's attention calculations, including reference frame constraints, key token retention, and region-based sampling [9][11]. - The token merging process reduces the number of tokens involved in attention calculations, while token unmerging ensures the integrity of dense 3D reconstruction outputs [15]. Group 4: Experimental Results - FastVGGT demonstrated a significant reduction in inference time and improved reconstruction quality across various datasets, including ScanNet-50, 7Scenes, and NRGBD [22]. - In point cloud reconstruction tasks, FastVGGT achieved a 4× speedup in inference time while maintaining reconstruction accuracy [18][22]. - The method also showed improvements in absolute trajectory error (ATE) and relative pose error (RPE) metrics, indicating enhanced performance in long sequence inference [24].