Point Cloud Noise and Outlier Removal for Image-Based 3D Reconstruction
| Autor: | Alexander Sorkine-Hornung, Mario Botsch, Changil Kim, Christopher Schroers, Katja Wolff, Olga Sorkine-Hornung, Henning Zimmer |
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| Rok vydání: | 2016 |
| Předmět: |
Noise measurement
Pixel business.industry Noise reduction 3D reconstruction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Point cloud 020207 software engineering 02 engineering and technology Iterative reconstruction 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Surface reconstruction Smoothing ComputingMethodologies_COMPUTERGRAPHICS Mathematics |
| Zdroj: | 3DV PUB-Publications at Bielefeld University |
| DOI: | 10.1109/3dv.2016.20 |
| Popis: | Point sets generated by image-based 3D reconstruction techniques are often much noisier than those obtained using active techniques like laser scanning. Therefore, they pose greater challenges to the subsequent surface reconstruction (meshing) stage. We present a simple and effective method for removing noise and outliers from such point sets. Our algorithm uses the input images and corresponding depth maps to remove pixels which are geometrically or photometrically inconsistent with the colored surface implied by the input. This allows standard surface reconstruction methods (such as Poisson surface reconstruction) to perform less smoothing and thus achieve higher quality surfaces with more features. Our algorithm is efficient, easy to implement, and robust to varying amounts of noise. We demonstrate the benefits of our algorithm in combination with a variety of state-of-the-art depth and surface reconstruction methods. |
| Databáze: | OpenAIRE |
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