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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Neural Implicit Surface Reconstruction for Reflective Surfaces |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | 3D reconstruction from calibrated multi-view images without 3D supervision is a long-standing problem in computer vision. Classical approaches, such as multi-view stereo (MVS), struggle to generate complete meshes for textureless or non-Lambertian surfaces due to poor correspondence matchings between different views. Following the seminal work of NeRF, multi-view 3D reconstruction combining neural implicit representations with volume rendering has emerged as a promising alternative, enabling flexible shape and appearance modeling. However, these methods face challenges in handling specularities and reflections on glossy surfaces. In this work, we introduce Ref-SDF, a volume rendering-based neural implicit surface reconstruction method capable of recovering challenging reflective surfaces. Ref-SDF extends the view-dependent appearance structure introduced in Ref-NeRF by incorporating SDF surface representation, resulting in both more photo-realistic rendering and accurate geometry. Our pipeline showcases superior performance in terms of geometry reconstruction quality and rendering quality when compared to state-of-the-art methods. Notably, our approach achieves these results without requiring additional geometric supervision, while remaining competitive with methods that rely on geometric cues. Thus, our method allows for broader applications in scenarios where geometric cues are not available and is not constrained by the quality of depth or normal maps computed by pretrained monocular estimators. |
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