Abstract
Abstract. Texture mapping techniques are used to achieve a high degree of realism for computer generated large-scale and detailed 3D surface models by extracting the texture information from photographic images and applying it to the object surfaces. Due to the fact that a single image cannot capture all parts of the scene, a number of images should be taken. However, texturing the object surfaces from several images can lead to lighting variations between the neighboring texture fragments. In this paper we describe the creation of a textured 3D scene from overlapping aerial images using a Markov Random Field energy minimization framework. We aim to maximize the quality of the generated texture mosaic, preserving the resolution from the original images, and at the same time to minimize the seam visibilities between adjacent fragments. As input data we use a triangulated mesh of the city center of Munich and multiple camera views of the scene from different directions.
Highlights
ObjectivesWe aim to maximize the quality of the generated texture mosaic, preserving the resolution from the original images, and at the same time to minimize the seam visibilities between adjacent fragments
In order to compare the roles of the two terms in energy function in Equation (9) we start with λ = 0
The situation is different when the truncation constant is set to T = 0.1. This parameter enforces the labeling F to keep a few regions with sigdoi:10.5194/isprsannals-II-3-W4-25-2015
Summary
We aim to maximize the quality of the generated texture mosaic, preserving the resolution from the original images, and at the same time to minimize the seam visibilities between adjacent fragments. In our paper we aim to texture a real-world and large-scale 3D city model by applying the high-resolution aerial photographic images. Because triangles may be visible in several images, we aim to find the image with the “best” texture for these faces
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