3D scene generation is a core technology for gaming, film/VFX, and VR/AR. Growing demand for rapid iteration, high-fidelity detail, and accessible content creation has further increased interest in this area. Existing methods broadly follow two paradigms - indirect 2D-to-3D reconstruction and direct 3D generation - but both are limited by weak structural modeling and heavy reliance on large-scale ground-truth supervision, often producing structural artifacts, geometric inconsistencies, and degraded high-frequency details in complex scenes. We propose GeoDiff3D, an efficient self-supervised framework that uses coarse geometry as a structural anchor and a geometry-constrained 2D diffusion model to provide texture-rich reference images. Importantly, GeoDiff3D does not require strict multi-view consistency of the diffusion-generated references and remains robust to the resulting noisy, inconsistent guidance. We further introduce voxel-aligned 3D feature aggregation and dual self-supervision to maintain scene coherence and fine details while substantially reducing dependence on labeled data. GeoDiff3D also trains with low computational cost and enables fast, high-quality 3D scene generation. Extensive experiments on challenging scenes show improved generalization and generation quality over existing baselines, offering a practical solution for accessible and efficient 3D scene construction.

翻译：三维场景生成是游戏、影视特效以及虚拟现实/增强现实领域的核心技术。快速迭代、高保真细节以及便捷内容创作需求的日益增长，进一步提升了该领域的研究热度。现有方法主要遵循两种范式——间接的二维到三维重建与直接的三维生成——但二者均受限于结构建模能力薄弱以及对大规模真实标注数据的强依赖，在复杂场景中常产生结构伪影、几何不一致性及高频细节退化等问题。本文提出GeoDiff3D，一种高效的自监督框架，其以粗糙几何体作为结构锚点，并利用几何约束的二维扩散模型提供纹理丰富的参考图像。值得注意的是，GeoDiff3D不要求扩散生成的参考图像具备严格的多视角一致性，并对由此产生的噪声化、非一致性引导保持鲁棒性。我们进一步引入体素对齐的三维特征聚合与双重自监督机制，在显著降低对标注数据依赖的同时，保持场景连贯性与精细细节。GeoDiff3D的训练计算成本较低，并能实现快速、高质量的三维场景生成。在复杂场景上的大量实验表明，相较于现有基线方法，本方法在泛化能力与生成质量上均有提升，为便捷高效的三维场景构建提供了实用解决方案。