Image-based Three-dimensional Reconstruction Research Articles

Texture-Mapping Error Removal Based on the BRIEF Operator in Image-Based Three-Dimensional Reconstruction

In image-based three-dimensional (3D) reconstruction, texture-mapping techniques can give the model realistic textures. When the geometric surface in some regions is not reconstructed, such as for moving cars, powerlines, and telegraph poles, the textures in the corresponding image are textured to other regions, resulting in errors. To solve this problem, this letter proposes an image consistency detection method based on the Binary Robust Independent Elementary Features (BRIEF) descriptor. The method is composed of two parts. First, each triangle in the mesh and its neighboring triangles are sampled uniformly to obtain sampling points. Then, these sampled points are projected into the visible image of the triangle, and the corresponding sampled points and their RGB color values are obtained on the corresponding image. Based on the sampled points on these images, a BRIEF descriptor is calculated for each image corresponding to that triangle. In the second step, the Hamming distance between these BRIEF descriptors is calculated, outliers are removed according to the method, and noisy images are also removed. In addition, we propose adding semantic information to Markov energy optimization to reduce errors further. The two methods effectively reduced errors in texture mapping caused by objects not reconstructed, improving the texture quality of 3D models.

Image-based 3D reconstruction for rail profile measurement

The routine inspection of railheads for defects such as wear and surface cracks is a tedious process, which, if not detected, can alter the wheel-rail contact interaction leading to catastrophic events. This study investigates and implements a railhead measurement method using image-based three-dimensional reconstruction, which enables rapid scanning of railheads and production of detail cross-sectional measurements for rail-wheel interface analysis. A complete workflow with a methodology for reconstructing railheads from images and extracting cross-sectional measurements from the reconstructed model is presented. In order to validate the proposed method in the field, a mobile automated system was equipped with an array of cameras specifically spaced to cover the areas of interest on the railhead. The system can automatically transverse along the railhead, acquiring images synchronously. Two case studies in the laboratory environment and the real railway site have been performed to evaluate the performance and accuracy against industry practices. The results show that the proposed method can accurately measure the railhead cross-sectional profile at an root mean square error (RMSE) less than 0.3 mm compared with MiniProf. Furthermore, continuous cross-sectional data and intuitive color information are provided by our method which can help inspectors to locate defects easily and more efficiently.

Seamless texture mapping algorithm for image-based three-dimensional reconstruction

Texture information plays an important role in rendering true objects, especially with the wide application of image-based three-dimensional (3-D) reconstruction and 3-D laser scanning. This paper proposes a seamless texture mapping algorithm to achieve a high-quality visual effect for 3-D reconstruction. At first, a series of image sets is produced by analyzing the visibility of triangular facets, the image sets are clustered and segmented into a number of optimal reference texture patches. Second, the generated texture patches are sequenced to create a rough texture map, then a weighting process is adopted to reduce the color discrepancies between adjacent patches. Finally, a multiresolution decomposition and fusion technique is used to generate the transition section and eliminate the boundary effect. Experiments show that the proposed algorithm is effective and practical for obtaining high-quality 3-D texture mapping for 3-D reconstruction. Compared with traditional methods, it maintains the texture clarity while eliminating the color seams, in addition, it also supports 3-D texture mapping for big data application.

Computer-aided tissue engineering: overview, scope and challenges.

Advances in computer-aided technology and its application with biology, engineering and information science to tissue engineering have evolved a new field of computer-aided tissue engineering (CATE). This emerging field encompasses computer-aided design (CAD), image processing, manufacturing and solid free-form fabrication (SFF) for modelling, designing, simulation and manufacturing of biological tissue and organ substitutes. The present Review describes some salient advances in this field, particularly in computer-aided tissue modeling, computer-aided tissue informatics and computer-aided tissue scaffold design and fabrication. Methodologies of development of CATE modelling from high-resolution non-invasive imaging and image-based three-dimensional reconstruction, and various reconstructive techniques for CAD-based tissue modelling generation will be described. The latest development in SFF to tissue engineering and a framework of bio-blueprint modelling for three-dimensional cell and organ printing will also be introduced.