Real-Time Variational Range Image Fusion and Visualization for Large-Scale Scenes Using GPU Hash Tables

We present a real-time pipeline for large-scale 3D scene reconstruction from a single moving RGB-D camera together with interactive visualization. Our approach combines a time and space efficient data structure capable of representing large scenes, a local variational update algorithm and a visualization system. The environment's structure is reconstructed by integrating the depth image of each camera view into a sparse volume representation using a truncated signed distance function, which is organized via a hash table. Noise from real-world data is efficiently eliminated by immediately performing local variational refinements on newly integrated data. The whole pipeline is able to perform in real-time on consumer-available hardware and allows for simultaneous inspection of the currently reconstructed scene.