RoomPlanner: Reachability-Aware View Sampling for Text-to-Room 3D Gaussian Splatting

In this paper, we propose RoomPlanner, the first fully au-tomatic 3D room generation framework for painlessly creating realisticindoor scenes with only short text as input. Without any manual layoutdesign or panoramic image guidance, our framework can generate explicitlayout criteria for rational spatial placement. We begin by introducing ahierarchical structure of language-driven agent planners that can auto-matically parse short and ambiguous prompts into detailed scene descrip-tions. These descriptions include raw spatial and semantic attributes foreach object and the background, which are then used to initialize 3Dpoint clouds. To position objects within bounded environments, we im-plement two arrangement constraints that iteratively optimize spatialarrangements, ensuring a collision-free and accessible layout solution.In the final rendering stage, we propose a novel ReachView Samplingstrategy for camera trajectory, along with the Interval Timestep FlowSampling (ITFS) strategy, to efficiently optimize the coarse 3D Gaussianscene representation. These approaches help reduce the total generationtime to under 30 minutes. Extensive experiments demonstrate that ourmethod can produce geometrically rational 3D indoor scenes, surpass-ing prior approaches in both rendering speed and visual quality whilepreserving editability. The code will be available at https://kaitlina-s.github.io/RoomPlanner/.