To avoid redundancy in these synthetic datasets, it is crucial that each element contains unique features and remains diverse from others during the synthesis stage. In this paper, we provide a thorough theoretical and empirical analysis of diversity within synthesized datasets. We argue that enhancing diversity can improve the parallelizable yet isolated synthesizing approach.

However, constructing a high-resolution occupancy map remains infeasible for large scenes due to computational constraints.

Image diffusion distillation achieves high-fidelity generation with very few sampling steps.

Morespecifically,3DGM formulates multitraverse environmental mapping as a robust 3D representation learning problem, treating pixels of the environment and objects as inliers and outliers, respectively.