Abstract: We present Acc3D to tackle the challenge of accelerating the diffusion process for generating 3D models from single images. To derive accurate reconstruction through few-step inference, we emphasize the critical issue as the modeling of the score function at the endpoints (states of the random noise). To tackle such an issue, we propose edge consistency, i.e., consistent predictions across the low signal-to-noise ratio region, to enhance a pre-trained diffusion model, enabling a distillation-based refinement of the endpoint score function. Building on those distilled diffusion models, we introduce an adversarial augmentation strategy to further enrich generation detail. The two modules complement each other, mutually reinforcing to elevate generative performance. Extensive experiments show that our Acc3D not only achieves over a $20\times$ increase in computational efficiency but also yields notable quality improvements, compared with state-of-the-art methods. Project webpage: https://acc3d-object.github.io/