Monocular three dimensional dense surface reconstruction by optical flow feedback

In this paper, a monocular 3D dense reconstruction technique based on optical flow feedback is proposed to achieve fast and accurate 3D stereoscopic modeling in the real scene. Corresponding pixel pairs are robustly matched by inter-frame optical flow fields, and the five-point algorithm is employed to determine relative pose of the moving camera, based on which sparse point cloud is generated and initial coarse mesh is built. In the proposed method, multi-view reconstruction is implemented from perspective of vision method on motion analysis. The reconstruction model is fed back to the reconstruction process, and the model is deformed by utilizing the bias-driven of each view. The coarse and inaccurate original mesh surface is adjusted to the exact surface through a dense non-rigid deformation. Under the Compute Unified Device Architecture (CUDA), the reconstruction algorithm is optimized in parallel mode by using the Graphic Processing Unit (GPU) hardware, and real-time performance of the reconstruction algorithm is significantly improved. The experimental results obtained in realistic indoor scenario demonstrate the effectiveness and accuracy of the proposed algorithm.