用几十张全景图快速重建整栋房子的三维结构
CasaMaestro: Multi-View Panoramas for House-Scale 3D Reconstruction
- 输入20-50张多视角全景图，直接预测深度和相机位姿
- 在真实与合成场景中均实现高质量全屋覆盖重建
- 适合需要快速获取房屋三维资产的闭环仿真应用
家用具身AI系统的发展推动了住宅空间快速、精确三维重建的需求，以支持导航、交互和长期任务执行。然而，传统针孔相机重建方法因视场有限，难以高效建模大户型室内空间，需数千张图像才能覆盖多房间，且长链增量对齐易引入误差。本文提出CasaMaestro（西班牙语意为“房屋主人”），首个支持多视角全景图的房屋级三维重建前馈模型。仅需20至50张稀疏多视角室内全景图作为输入，即可直接预测度量深度与相机位姿，实现全屋快速点云重建。实验表明，CasaMaestro在真实与合成场景中均能稳健生成高质量结果，可为获取房屋级三维室内资产提供坚实基础，适用于闭环仿真应用。
原文摘要 · Abstract (English)
The rise of home-deployed embodied AI systems is driving a growing need for fast, metric 3D reconstruction of residential spaces to support navigation, interaction, and long-horizon task execution. However, the commonly used pinhole-camera 3D reconstruction pipelines struggle to model large indoor residences efficiently due to their limited field of view, to which achieving full coverage across multiple rooms often requires thousands of images and incurs drift from long chains of incremental alignment. In this work, we present CasaMaestro (Spanish words meaning ``house'' and ``master''), a feedforward model that can take only twenty to fifty sparse multi-view indoor panoramas as input and directly predicts metric depth along with camera poses, allowing fast point-cloud reconstruction of the entire house with full coverage. CasaMaestro is the first model that supports house-scale reconstruction with multi-view panoramas. Experiments show that CasaMaestro can robustly provide high quality results in both real-world and synthetic scenes, which can serve as a strong foundation for acquiring house-scale 3D indoor assets to be applied in close-loop simulation.
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