Table reconstruction (TR) aims to extract cell contents and logical structure from table images. Existing table reconstruction methods are superior in recognizing logical structure, but they are inhibited by slow decoding speed, error accumulation in long sequences, and varying cell sizes in table images, which are critical for subsequent table reconstruction. To address these problems, we propose TableRocket, a table reconstruction framework that includes an end-to-end layout cell instance segmentation module based on multi-stage query mechanisms and a non-autoregressive (NAR) logical structure generation module. TableRocket employs an innovative method for model layout cell segmentation by introducing dynamic candidate boxes and features to more accurately address the problem of varying cell sizes in table images, and has developed an algorithm specifically for logical structure generation based on layout cell bounding boxes. Extensive experimental results demonstrate that TableRocket achieves significantly faster inference speeds, while maintaining comparable performance to the auto-regressive model.