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

Abstract

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.

Keywords

• 3D reconstruction
• texture mapping
• BRIEF descriptors
• outlier detection