Anderson Acceleration of Test-Time Training for Feed-Forward 3D Reconstruction

Feed-forward 3D reconstruction predicts a scene representation from a few input views in a single forward pass, enabling efficient reconstruction without scene-specific optimization. Despite their efficiency, these methods often suffer from limited reconstruction quality and generalization to unseen scenes. To address this limitation, recent approaches introduce test-time training (TTT), which leverages supervision from test-time observations to adapt the model to each target scene. However, this scene-specific adaptation substantially increases inference time and diminishes the efficiency advantage of feed-forward reconstruction. To this end, we propose AA-TTT, a test-time training framework that formulates TTT as a fixed-point iteration and accelerates the process via Anderson acceleration. By directly exploiting the TTT trajectory, AA-TTT is applicable to a wide range of feed-forward 3D reconstruction systems. Extensive experiments on four representative TTT-based reconstruction systems demonstrate that AA-TTT consistently preserves reconstruction quality while achieving an average 1.4$\times$ test-time training speed across diverse reconstruction frameworks.