Efficient And Scalable Depthmap Fusion

The estimation of a complete 3D model from a set of depthmaps is a data intensive task aimed at mitigating measurement noise in the input data by leveraging the inherent redundancy in overlapping multi-view observations. In this paper we propose an efficient depthmap fusion approach that reduces the memory complexity associated with volumetric scene representations. By virtue of reducing the memory footprint we are able to process an increased reconstruction volume with greater spatial resolution. Our approach also improves upon state of the art fusion techniques by approaching the problem in an incremental online setting instead of batch mode processing. In this way, are able to handle an arbitrary number of input images at high pixel resolution and facilitate a streaming 3D processing pipeline. Experiments demonstrate the effectiveness of our proposal both at 3D modeling from internet-scale crowd source data as well as close-range 3D modeling from high resolution video streams.