Efficient Estimation and Representation of 3D model with Sensor Fusion

We propose a novel method for 3d modeling using a fusion of a laser range sensor, a camera, and a flashlight. This combination provides dense normals and surface colors that can be mapped on a 3d model, whereas conventional sensors only output point clouds of the 3d geometry. Furthermore, the fusion enables formulations to be made simply and practically. Multi-view photometric stereo is used for estimating the fine normal distribution with a basic shape measured by the laser range sensor. Our photometric stereo can easily handle near-light formulation and specularity. Detailed surfaces can be shown by applying the normal map as bump mapping to the basic shape. Robust estimation and clustering are used for estimating reflection parameters. Results demonstrate that our method can estimate highly accurate reflection parameters and provide fine surface appearances using only a small amount of data. The effectiveness of our method is shown with an application of 3d content.