A Hierarchical Optimization Algorithm Based on GPU for Real-Time 3D Reconstruction

In machine vision sensing system, it is important to realize high-quality real-time 3D reconstruction in large-scale scene. The recent online approach performed well, but scaling up the reconstruction, it causes pose estimation drift, resulting in the cumulative error, usually requiring a large number of off-line operation to completely correct the error, reducing the reconstruction performance. In order to optimize the traditional volume fusion method and improve the old frame-to-frame pose estimation strategy, this paper presents a real-time CPU to Graphic Processing Unit reconstruction system. Based on a robust camera pose estimation strategy, the algorithm fuses all the RGB-D input values into an effective hierarchical optimization framework, and optimizes each frame according to the global camera attitude, eliminating the serious dependence on the tracking timeliness and continuously tracking globally optimized frames. The system estimates the global optimization of gestures (bundling) in real-time, supports for robust tracking recovery (re-positioning), and re-estimation of large-scale 3D scenes to ensure global consistency. It uses a set of sparse corresponding features, geometric and ray matching functions in one of the parallel optimization systems. The experimental results show that the average reconstruction time is 415 ms per frame, the ICP pose is estimated 20 times in 100.0 ms. For large scale 3D reconstruction scene, the system performs well in online reconstruction area, keeping the reconstruction accuracy at the same time.