Robust Deep Learning-based Methods for Non-Rigid Shape Correspondence
| dc.contributor.author | Attaiki, Souhaib | |
| dc.date.accessioned | 2025-01-13T10:08:57Z | |
| dc.date.available | 2025-01-13T10:08:57Z | |
| dc.date.issued | 2024-10-10 | |
| dc.description.abstract | The automatic processing and analysis of 3D shapes is a critical area of research with significant implications for fields such as medical imaging, virtual reality, and computer graphics. A primary challenge in this domain is the efficient comparison of non-rigid shapes, which involves establishing correspondences between surfaces undergoing complex deformations. This dissertation enhances the state-of-the-art in non-rigid shape matching by leveraging deep learning within the functional map framework. Previous deep functional map methods struggle with partial shapes, decoding the information in their probe functions, and utilizing large-scale datasets for pretraining, among other issues. To overcome these obstacles, our work contributes five significant advancements to the field of deep functional maps. First, we introduce Deep Partial Functional Maps (DPFM), a novel architecture that enhances communication between source and target shapes, particularly adept at handling partial shapes with non-rigid deformations. Second, we present the Neural Correspondence Prior (NCP), which employs neural networks as a prior to propose a general, unsupervised method for shape matching, especially suitable for sparse and non-isometric data. Additionally, we analyze the features learned through deep functional maps and suggest straightforward modifications to the pipeline that extend the utility of these features beyond their traditional roles. Furthermore, we tackle the challenge of input feature robustness by exploring the pre-training of generalizable local features on large datasets of rigid shapes, thus boosting performance on non-rigid shape analysis tasks. We also introduce a zero-shot method for non-rigid shape matching that operates independently of any pretraining steps or datasets. Together, these innovations provide robust and efficient solutions for non-rigid shape matching, addressing long-standing challenges and broadening the application of these methods to diverse real-world datasets and applications. | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/3607102 | |
| dc.language.iso | en | |
| dc.publisher | Ecole Polytechnique | |
| dc.title | Robust Deep Learning-based Methods for Non-Rigid Shape Correspondence | |
| dc.type | Thesis |