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Online Reconstruction of Indoor Scenes With Local Manhattan Frame Growing

We propose an efficient approach for robust reconstruction of indoor scenes by taking advantage of the geometric relation between consecutive Manhattan keyframes and local pose refinement to improve the accuracy and fidelity of the reconstructed models. At the core of our framework, we have a Local Manhattan Frame Growing system, which finds the principal directions of the scene and aligns point clouds with the dominant plane, and a Local Pose Optimization, which refines the pose estimation for a specific range of frames. During the reconstruction process, we use Manhattan keyframes for a planar pre-alignment to provide a robust initialization for the final surface registration. All Manhattan keyframes are integrated using a frame-to-model scheme to create local models based on the refined camera poses. The final dense model is reconstructed by adopting a geometric registration between local segments and integrating them into a global frame. The experimental results show the effectiveness of our approach to reduce the cumulative registration error and overall geometric drift.