In recent years, visual SLAM technology has matured significantly. SLAM systems usually depend on natural feature points to acquire precise motion information, however, these methods frequently encounter tracking failures in scenes characterized by weak or repetitive textures. This paper proposes a Marker-based visual SLAM system by fusing Marker-based and feature point-based Cues. We use Marker-point cues in tracking and extract Marker-plane cues from geometric lines in mapping. ORB feature points, marker features, and plane features collaboratively contribute to local map optimization. Our method has been compared with the latest SLAM systems on both the public dataset and our dataset. The results demonstrate that our method improves accuracy in scenes with weak textures, and enhances robustness in challenging texture-less regions.
Stereo matching for depth estimation is a fundamental vision problem. Recent works focus on deep learning to improve accuracy, but most networks encountered the difficulty of poor generalization ability and high computational cost especially on high resolution images. RAFTStereo achieves great advantages in these two aspects, but still can be improved further. In this paper, we revise the residual block of RAFT-Stereo in its feature extractors to improve the performance in underwater scenarios. Specifically, we choose an iterative Attentional Feature Fusion module to utilize the global information in feature fusion. To justify our work, we test our networks on ETH3D benchmark and our own underwater dataset, which demonstrates the superiority of our model as compared to the state-of-the-art baselines. Eventually, comparing to original RAFT-Stereo, our results on ETH3D benchmark outperform by 13.1% on the default metric bad 1-pixel error (percentage of pixels with end-point-errors greater than 1px) and results on our underwater dataset reduce the average error by 16.9%.
In underwater computer vision systems, the camera is usually housed in a watertight case with a glass interface between water and air, which causes the light rays to refract, affecting image formation geometrically. In this paper, we propose an approach to calibrate the housing parameters of a flat-port underwater camera for a reasonable underwater refractive camera model. Our approach is specifically designed to deal with the refraction effect caused by the light traveling in different mediums. In this camera model, the calibrated housing parameters include the distance between the camera and glass interface and the normal of the glass interface in the camera coordinate system. We employ an underwater binocular vision system to calibrate the housing parameters in two steps. First, the distance to the glass interface is estimated by capturing a checkerboard image and computing the distance from the camera center to a known flat plane. Then, the normal of the glass interface is calculated by a multiobjective optimization method using geometric constraints in the underwater binocular stereo system. Extensive experiments have been conducted on our underwater dataset and the results verify the effectiveness of the proposed scheme for underwater camera calibration and three-dimensional measurements.
Photometric stereo recovers surface shape for well details but fails when the images are captured with relative motion between camera and object. Therefore, we propose a dynamic photometric stereo method for 3D reconstruction of flat bas-relief objects. The key contributions of our work are to build a unified world coordinate system between multiview images by structure from motion with eliminating the mismatching points caused by the shadow, and to establish the pixel-level dense match, utilizing the homography between the flat object in two views. Finally, we can use the classic photometric stereo to obtain a high-quality 3D reconstruction result. The effectiveness of our method is verified on the real datasets.
Light sources’ position calibration is critical to photometric stereo. This paper presents a method for the light source position calibration based on a standard block. Unlike prior works that use mirror spheres, we use a calibration target consisting of a flat plane and a standard block placed on the plane with known length. By analyzing and reasoning the shadow produced by the standard block under a fixed light, we find that the relations of shadow and light conform to the principle of trigonometric geometry. The near light source’s position can be obtained according to the position relationship between the line segment and the inflection point in the shadow. Compared with other methods, our method is convenient and fast, and has better results.
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.
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.
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.
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