CLOUD-BASED DEPTH SENSING QUALITY FEEDBACK FOR INTERACTIVE 3D RECONSTRUCTION

In this paper we propose a cloud-based approach to improve the 3D reconstruction capability of handheld devices with real-time depth sensors. We attempt to characterize the quality of 3D information captured by real time depth sensing devices, and in particular examine how sensors from Prime Sense and Canesta measure distances, and derive simple analytical models on performance limitations for each. We also study the factors that affect depth sensing quality when these devices are used to incrementally build larger or denser 3D models. Empirical experiments confirm our analysis. Our findings allow us to design a quality metric which can interactively inform users to guide them on how to optimize the quality of their captured 3D content.