Group re-identification (GReID) attempts to correctly associate groups with the same members under different cameras. The main challenge is how to resist the membership and layout variations. Existing works attempt to incorporate layout modeling on the basis of appearance features to achieve robust group representations. However, layout ambiguity is introduced because these methods only consider the 2D layout on the imaging plane. In this paper, we overcome the above limitations by 3D layout modeling. Specifically, we propose a novel 3D transformer (3DT) that reconstructs the relative 3D layout relationship among members, then applies sampling and quantification to preset a series of layout tokens along three dimensions, and selects the corresponding tokens as layout features for each member. Furthermore, we build a synthetic GReID dataset, City1M, including 1.84M images, 45K persons and 11.5K groups with 3D annotations to alleviate data shortages and poor annotations. To the best of our knowledge, 3DT is the first work to address GReID with 3D perspective, and the City1M is the currently largest dataset. Several experiments show the superiority of our 3DT and City1M. Our project has been released on https://github.com/LinlyAC/City1M-dataset.