The task of 3D shape completion involves completing missing regions of an object from partial observation. The current methods accomplish this task by modeling latent completion distributions based on an autoregressive model. However, this approach often struggles with geometric details, as it represents 3D shapes with variable latent sequences, leading to gaps (local missing) in the completed shape. In this paper, we introduce a multiple 3D shape completion method using a transformer-based autoregressive model and a fixed-length sparse irregular latent sequence. Experiments demonstrate that our method outperforms state-of-the-art methods in terms of both quality and fidelity.