ArticleSensors (Basel, Switzerland)2021
Robust Texture Mapping Using RGB-D Cameras.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Efficient Mesh Reconstruction and Texturing of Oracle Bones.Sensors (Basel, Switzerland) · 2026Article
- A Sequential Color Correction Approach for Texture Mapping of 3D Meshes.Sensors (Basel, Switzerland) · 2023Article
- A Robust 3D-Based Color Correction Approach for Texture Mapping Applications.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
5 authors.
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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.
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Registered trials
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