Real-time volumetric reconstruction of Manhattan indoor scenes from depth sequences

Abstract

We propose an efficient 3D modeling method to support real-time volumetric reconstruction of indoor scenes based on sequential depth sequences captured from a RGB-D camera. Specifically, we want to reduce the cumulative error from sequential ICP registration due to noise and outliers in the depth data. We take advantage of the Manhattan frame assumption valid in most indoor scenes that can be used to facilitate large scale 3D surface registration. In our approach, the Manhattan frame is extracted from each depth frame and used for plane-to-plane frame alignment to initialize point-to-plane ICP surface registration. Experimental results on three different indoor datasets including LIDAR ground-truth data demonstrate the advantages of the proposed algorithm over the original ICP-based approaches to volumetric reconstruction.