Abstract
The creation of a textured 3D mesh from a set of RGD-D images often results in textured meshes that yield unappealing visual artifacts. The main cause is the misalignments between the RGB-D images due to inaccurate camera pose estimations. While there are many works that focus on improving those estimates, the fact is that this is a cumbersome problem, in particular due to the accumulation of pose estimation errors. In this work, we conjecture that camera poses estimation methodologies will always display non-neglectable errors. Hence, the need for more robust texture mapping methodologies, capable of producing quality textures even in considerable camera misalignments scenarios. To this end, we argue that use of the depth data from RGB-D images can be an invaluable help to confer such robustness to the texture mapping process. Results show that the complete texture mapping procedure proposed in this paper is able to significantly improve the quality of the produced textured 3D meshes.
Highlights
RGB-D sensors have had an astounding increase in popularity on behalf of both computer graphics as well as computer vision researchers [1,2]
Our approach directly employs depth information to conduct the process of texture mapping. It is for this reason that we describe the process as texture mapping using RGB-D cameras
This work proposed a novel approach for texture mapping of 3D models
Summary
RGB-D sensors have had an astounding increase in popularity on behalf of both computer graphics as well as computer vision researchers [1,2]. RGB-D sensors were initially introduced in the field of home entertainment and gaming [3] Since their usage has expanded to many other areas such as robotics [4,5], agriculture [6,7], autonomous driving [8,9], human action recognition [10,11], object recognition [12,13] and 3D scene reconstruction [14,15,16,17], to name a few. The term has been used in computer vision as well, in the context of cases where color taken from photographs is mapped onto reconstructed 3D models on real scenes, e.g., [15,16,17]
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