The VGGT (Visual Geometry Grounded Transformer) model has been garnering attention for its impressive capabilities in 3D scene reconstruction by leveraging a large transformer architecture without specific 3D inductive biases. The paper emphasizes the significant computational resources used for training (over 64 A100 GPUs for nine days) and the extensive dataset involved. User comments reflect a blend of optimism regarding its potential applications, such as enhancing phone-based 3D scanning, alongside skepticism about its performance in complex outdoor environments where landmarks may influence results. There's enthusiasm for the model's ability to reconstruct scenes with fewer images, contrasting with traditional methods requiring extensive calibration and numerous inputs. Overall, VGGT represents an evolution in 3D reconstruction technology, balancing the interface between AI and traditional photogrammetry approaches, and it opens discussions about future use cases and optimization for specific applications like splats and hybrid systems.