The power of indoor crowd: Indoor 3D maps from the crowd

Remarkable progress was made with smartphones in the last few years. Modern smartphones are now equipped with high-resolution cameras and various micro-electrical sensors that open up new mobile application possibilities. In this work, we address a critical task of reconstruct indoor large-scale 3D model from crowd-sourced images. We propose, design, and implement IndoorCrowd, a smartphone empowered crowdsourcing system for large-scale indoor 3D scene reconstruction. IndoorCrowd fills a gap in current cloud-based 3D reconstruction systems as it ensures at mobile side that the captured image set fulfills desired quality for indoor large-scene 3D reconstruction. At the cloud side, we deploy an automated image-based 3D reconstruction pipeline, which generates 3D models from images and sensor data. Moreover, we provide an intuitive online annotation tool that allows easy image labeling. We present that these labeling information combined with sensor data helps IndoorCrowd reduce the total processing time greatly.