In this paper, we propose a method for accurate 3D reconstruction based on Photometric Stereo. Instead of applying the global least square solution on the entire over-determined system, we randomly sample the images to form a set of overlapping groups and recover the surface normal for each group using the least square method. We then employ fourdimensional Tensor Robust Principal Component Analysis (TenRPCA) to obtain the accurate 3D reconstruction. Our method outperforms global least square in handling sparse noises such as shadows and specular highlights. Experiments demonstrate the reconstruction accuracy of our approach.
Metal corrosion can cause many problems, how to quickly and effectively assess the grade of metal corrosion and timely remediation is a very important issue. Typically, this is done by trained surveyors at great cost. Assisting them in the inspection process by computer vision and artificial intelligence would decrease the inspection cost. In this paper, we propose a dataset of metal surface correction used for computer vision detection and present a comparison between standard computer vision techniques by using OpenCV and deep learning method for automatic metal surface corrosion grade estimation from single image on this dataset. The test has been performed by classifying images and calculating the accuracy for the two different approaches.
Laser triangulation and photometric stereo are commonly used three-dimensional (3-D) reconstruction methods, but they bear limitations in an underwater environment. One important reason is due to the refraction occurring at the interface (usually glass) of the underwater housing. The image formation process does not follow the commonly used pinhole camera model, and the image captured by the camera is a refracted projection of the object. We introduce a flat refraction model to describe the geometric relation between the refracted image and the real object. The model parameters were estimated in a calibration step with a standard chessboard. The proposed geometric relation is used for rebuilding underwater 3-D shapes in laser triangulation and photometric stereo. The experimental results indicate that our method can effectively correct the distortion in underwater 3-D reconstruction.
Classical photometric stereo requires uniform collimated light, but point light sources are usually employed in practical setups. This introduces errors to the recovered surface shape. We found that when the light sources are evenly placed around the object with the same slant angle, the main component of the errors is the low-frequency deformation, which can be approximately described by a quadratic function. We proposed a postprocessing method to correct the deviation caused by the nonuniform illumination. The method refines the surface shape with prior information from calibration using a flat plane or the object itself. And we further introduce an optimization scheme to improve the reconstruction accuracy when the three-dimensional information of some locations is available. Experiments were conducted using surfaces captured with our device and those from a public dataset. The results demonstrate the effectiveness of the proposed approach.
Underwater images are blurred due to light scattering and absorption. Image restoration is therefore important in many underwater research and practical tasks. In this paper, we propose an effective two-stage method to restore underwater scene images. Based on an underwater light propagation model, we first remove backscatter by fitting a binary quadratic function. Then we eliminate the forward scattering and non-uniform lighting attenuation using blue-green dark channel prior. The proposed method requires no additional calibration and we show its effectiveness and robustness by restoring images captured under various underwater scenes.