The paper introduces a novel approach for 3D scene generation by leveraging high-dimensional implicit 3D latent spaces derived from 2D foundation models such as DINOv2, SigLIP2, and DA3, addressing limitations of traditional 2D multi-view diffusion methods. These methods suffer from redundancy in 2D representations and spatial inconsistency due to latent space constraints. The authors adapt diffusion transformers to perform direct modeling in the 3D latent space, enabling spatially consistent and efficient scene generation. Experiments demonstrate improved efficiency and spatial coherence compared to 2D-based approaches, with a focus on analyzing how 3D latent representations enhance performance. Scalability is validated across varying representation dimensions and model capacities, showing robustness. The method reduces token redundancy while maintaining detail, as evidenced by quantitative metrics and visual results. Submitted to arXiv in April 2026 (v1) and revised in October 2026 (v2), the work is attributed to Dongxu Wei, Qi Xu, Zhiqi Li, Hangning Zhou, and Cong Qiu, offering a scalable framework for 3D scene generation at scale.