LucidDreamer: Domain-Free Generation of 3D Gaussian Splatting Scenes
Jaeyoung Chung, Suyoung Lee, Hyeongjin Nam, Jaerin Lee, Kyoung Mu Lee
Abstract
Generating high-quality 3D scenes is a critical challenge in computer vision, driven by advances in 3D graphics and the growing demand for immersive environments. While object-centric 3D generation has achieved significant progress, scene generation remains difficult due to the scarcity of large-scale 3D scene datasets and scalability constraints of conventional 3D representations, which hinder efficient large-scale expansion. To address these challenges, we propose LucidDreamer, a novel pipeline that synthesizes diverse, high-quality, and expandable 3D scenes using a unified 3D Gaussian splatting representation. Our approach employs an iterative Navigation-Dreaming-Alignment process, leveraging 2D image generation and depth estimation to construct photorealistic, scalable 3D environments. By iteratively generating images and navigating through the scene, LucidDreamer fully utilizes the power of image generation models, enabling the creation of highly detailed and expandable 3D scenes. LucidDreamer supports various input modalities, including text, RGB, and RGBD, and enables dynamic modifications during generation. Experimental results demonstrate that LucidDreamer outperforms existing methods in generating high-quality, diverse, structurally consistent, and navigable 3D scenes.