Video-Based Outdoor Human Reconstruction

Abstract

A human body scanning system of great practical convenience, which can be used in an outdoor environment, is proposed. The system uses only a single conventional video camera without the aid of special sensors or controlled illuminations. We leverage the structure from motion calibration results directly and improve the available video-based dense 3D reconstruction by integrating the surface smoothness constraints. The point cloud reinforcement is proposed to detect and adjust the conflict point data for the slender and shaky body parts. Combined with the silhouette adaptation, the proposed point cloud reinforcement achieves reasonable and plausible mesh reconstruction on these challenging parts. We further introduce the close-shot frames to refine the prereconstructed mesh model, leading to a colored watertight model. The overall system is approximate to automatic since only one or two times of painting brush interaction are required for robust and high-quality multiview image segmentation. The experiment results on various test sequences demonstrate the effectiveness and the robustness of the proposed method, even under very challenging scenarios when shaking body, varying illumination, and textureless regions occur.