My publications

2025

1. CVPR 2025

   

   EchoMatch: Partial-to-Partial Shape Matching via Correspondence Reflection

   Yizheng Xie^†, Viktoria Ehm^†, Paul Roetzer , and 4 more authors

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Jun 2025

   Abs Bib Website

   Finding correspondences between 3D shapes is a crucial problem in computer vision and graphics. While most research has focused on finding correspondences in settings where at least one of the shapes is complete, the realm of partial-to-partial shape matching remains under-explored. Yet, it is important since in many applications shapes are only observed partially due to occlusion or scanning. Finding correspondences between partial shapes comes with an additional challenge: We not only want to identify correspondences between points on either shape but also have to determine which points of each shape actually have a partner. To tackle this challenging problem, we present EchoMatch, a novel framework for partial-to-partial shape matching that incorporates the concept of correspondence reflection to enable an overlap prediction within a functional map framework. With this approach, we show that we can outperform current SOTA methods in challenging partial-to-partial shape matching problems.

   @inproceedings{xie2025echomatch,
     author = {Xie, Yizheng and Ehm, Viktoria and Roetzer, Paul and Amrani, Nafie El and Gao, Maolin and Bernard, Florian and Cremers, Daniel},
     title = {EchoMatch: Partial-to-Partial Shape Matching via Correspondence Reflection},
     booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
     month = jun,
     year = {2025}
   }

2024

1. Arxiv

   

   Beyond Complete Shapes: A quantitative Evaluation of 3D Shape Matching Algorithms

   Viktoria Ehm^†, Nafie El Amrani^†, Yizheng Xie , and 8 more authors

   Nov 2024

   Abs arXiv Bib Website

   Finding correspondences between 3D shapes is an important and long-standing problem in computer vision, graphics and beyond. While approaches based on machine learning dominate modern 3D shape matching, almost all existing (learning-based) methods require that at least one of the involved shapes is complete. In contrast, the most challenging and arguably most practically relevant setting of matching partially observed shapes, is currently underexplored. One important factor is that existing datasets contain only a small number of shapes (typically below 100), which are unable to serve data-hungry machine learning approaches, particularly in the unsupervised regime. In addition, the type of partiality present in existing datasets is often artificial and far from realistic. To address these limitations and to encourage research on these relevant settings, we provide a generic and flexible framework for the procedural generation of challenging partial shape matching scenarios. Our framework allows for a virtually infinite generation of partial shape matching instances from a finite set of shapes with complete geometry. Further, we manually create cross-dataset correspondences †: These authors contributed equally to this work. between seven existing (complete geometry) shape matching datasets, leading to a total of 2543 shapes. Based on this, we propose several challenging partial benchmark settings, for which we evaluate respective state-of-the-art methods as baselines.

   @article{ehm2024becos,
     title = {Beyond Complete Shapes: A quantitative Evaluation of 3D Shape Matching Algorithms},
     author = {Ehm, Viktoria and El Amrani, Nafie and Xie, Yizheng and Bastian, Lennart and Gao, Maolin and Wang, Weikang and Sang, Lu and Cao, Dongliang and L{\"a}hner, Zorah and Cremers, Daniel and others},
     month = nov,
     year = {2024}
   }

2. MICCAI 2024

   

   A Universal and Flexible Framework for Unsupervised Statistical Shape Model Learning

   Nafie El Amrani, Dongliang Cao, and Florian Bernard

   In Medical Image Computing and Computer Assisted Intervention (MICCAI) , Oct 2024

   Abs Bib PDF Code

   We introduce a novel unsupervised deep learning framework for constructing statistical shape models (SSMs). Although unsupervised learning-based 3D shape matching methods have made a major leap forward in recent years, the correspondence quality of existing methods does not meet the demanding requirements necessary for the construction of SSMs of complex anatomical structures. We address this shortcoming by proposing a novel deformation coherency loss to effectively enforce smooth and high-quality correspondences during neural network training. We demonstrate that our framework outperforms existing methods in creating high-quality SSMs by conducting extensive experiments on five challenging datasets with varying anatomical complexities. Our proposed method sets the new state of the art in unsupervised SSM learning, offering a universal solution that is both flexible and reliable. Our source code is publicly available at https://github.com/NafieAmrani/FUSS.

   @inproceedings{elamrani2024fuss,
     author = {El Amrani, Nafie and Cao, Dongliang and Bernard, Florian},
     title = {A Universal and Flexible Framework for Unsupervised Statistical Shape Model Learning},
     booktitle = {Medical Image Computing and Computer Assisted Intervention (MICCAI)},
     month = oct,
     year = {2024}
   }

3. CVPR 2024

   

   Spectral Meets Spatial: Harmonising 3D Shape Matching and Interpolation

   Dongliang Cao, Marvin Eisenberger, Nafie El Amrani , and 2 more authors

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Jun 2024

   Abs arXiv Bib Code

   Although 3D shape matching and interpolation are highly interrelated, they are often studied separately and applied sequentially to relate different 3D shapes, thus resulting in sub-optimal performance. In this work we present a unified framework to predict both point-wise correspondences and shape interpolation between 3D shapes. To this end, we combine the deep functional map framework with classical surface deformation models to map shapes in both spectral and spatial domains. On the one hand, by incorporating spatial maps, our method obtains more accurate and smooth point-wise correspondences compared to previous functional map methods for shape matching. On the other hand, by introducing spectral maps, our method gets rid of commonly used but computationally expensive geodesic distance constraints that are only valid for near-isometric shape deformations. Furthermore, we propose a novel test-time adaptation scheme to capture both pose-dominant and shape-dominant deformations. Using different challenging datasets, we demonstrate that our method outperforms previous state-of-the-art methods for both shape matching and interpolation, even compared to supervised approaches.

   @inproceedings{cao2024spectral,
     author = {Cao, Dongliang and Eisenberger, Marvin and El Amrani, Nafie and Cremers, Daniel and Bernard, Florian},
     title = {Spectral Meets Spatial: Harmonising 3D Shape Matching and Interpolation},
     booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
     month = jun,
     year = {2024}
   }