Efficient Online Surface Correction for Real-time Large-Scale 3D Reconstruction

Understand

State-of-the-art methods for large-scale 3D reconstruction from RGB-D sensors usually reduce drift in camera tracking by globally optimizing the estimated camera poses in real-time without simultaneously updating the reconstructed surface on pose changes.

• We propose an efficient on-the-fly surface correction method for globally consistent dense 3D reconstruction of large-scale scenes.
• Our approach uses a dense Visual RGB-D SLAM system that estimates the camera motion in real-time on a CPU and refines it in a global pose graph optimization.
• Consecutive RGB-D frames are locally fused into keyframes, which are incorporated into a sparse voxel hashed Signed Distance Field (SDF) on the GPU.