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Polynomial Image-Based Rendering for non-Lambertian Objects

par Fachada, Sarah ;Bonatto, Daniele ;Teratani, Mehrdad ;Lafruit, Gauthier
Référence VCIP 2021(5-8 December 2021: Munich, Germany), 2021 IEEE International Conference on Visual Communications and Image Processing (VCIP 2021)
Publication Publié, 2021-12-05

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Résumé :

Non-Lambertian objects present an aspect which depends on the viewer’s position towards the surrounding scene. Contrary to diffuse objects, their features move non-linearly with the camera, preventing rendering them with existing Depth Image-Based Rendering (DIBR) approaches, or to triangulate their surface with Structure-from-Motion (SfM). In this paper, we propose an extension of the DIBR paradigm to describe these non-linearities, by replacing the depth maps by more complete multi-channel ”non-Lambertian maps”, without attempting a 3D reconstruction of the scene. We provide a study of the importance of each coefficient of the proposed map, measuring the trade-off between visual quality and data volume to optimally render non-Lambertian objects. We compare our method to other state-of-the-art image based rendering methods and outperform them with promising subjective and objective results on a challenging dataset.