CasaMaestro: Multi-View Panoramas for House-Scale 3D Reconstruction

The rise of home-deployed embodied AI systems is driving agrowing need for fast, metric 3D reconstruction of residential spaces tosupport navigation, interaction, and long-horizon task execution. How-ever, the commonly used pinhole-camera 3D reconstruction pipelinesstruggle to model large indoor residences efficiently due to their limitedfield of view, to which achieving full coverage across multiple rooms oftenrequires thousands of images and incurs drift from long chains of incre-mental alignment. In this work, we present CasaMaestro (Spanish wordsmeaning “house” and “master”), a feedforward model that can take onlytwenty to fifty sparse multi-view indoor panoramas as input and directlypredicts metric depth along with camera poses, allowing fast point-cloudDA3-AUC30 Pi3-AUC30 VGGT-AUC30Yaw Step 90° Yaw Step 45° Yaw Step 15° DA3-time Pi3-time VGGT-timeMulti Viewpoints Single Viewpoint0.9 8000.8 7000.7 6000.65000.54000.43000.30.2 2000.1 1000 0 (s)AUC Yaw step 90 Yaw step 45 Yaw step 15 TimesFig. 2: Illustration of existing problems. Left visualization shows pinhole modelseither face limited FoV in sparse capture or accumulative error in dense sequence. Rightfigure shows pose accuracy and processing time under different input density.reconstruction of the entire house with full coverage. CasaMaestro isthe first model that supports house-scale reconstruction with multi-viewpanoramas. Experiments show that CasaMaestro can robustly providehigh quality results in both real-world and synthetic scenes, which canserve as a strong foundation for acquiring house-scale 3D indoor assetsto be applied in close-loop simulation.