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DeepShapeMatchingKit: Accelerated Functional Map Solver and Shape Matching Pipelines Revisited
Yizheng Xie, Lennart Bastian, Congyue Deng +3
Deep functional maps, leveraging learned feature extractors and spectral correspondence solvers, are fundamental to non-rigid 3D shape matching. Based on an analysis of open-source…
RINO: Rotation-Invariant Non-Rigid Correspondences
Maolin Gao, Shao Jie Hu-Chen, Congyue Deng +3
Dense 3D shape correspondence remains a central challenge in computer vision and graphics as many deep learning approaches still rely on intermediate geometric features or handcraf…
Beyond Complete Shapes: A Benchmark for Quantitative Evaluation of 3D Shape Surface Matching Algorithms
Viktoria Ehm, Nafie El Amrani, Yizheng Xie +9
Finding correspondences between 3D deformable shapes is an important and long-standing problem in geometry processing, computer vision, graphics, and beyond. While various shape ma…
CoE: Deep Coupled Embedding for Non-Rigid Point Cloud Correspondences
Huajian Zeng, Maolin Gao, Daniel Cremers
The interest in matching non-rigidly deformed shapes represented as raw point clouds is rising due to the proliferation of low-cost 3D sensors. Yet, the task is challenging since p…
Enhancing Surface Neural Implicits with Curvature-Guided Sampling and Uncertainty-Augmented Representations
Lu Sang, Abhishek Saroha, Maolin Gao +1
Neural implicit representations have become a popular choice for modeling surfaces due to their adaptability in resolution and support for complex topology. While previous works ha…
Partial-to-Partial Shape Matching with Geometric Consistency
Viktoria Ehm, Maolin Gao, Paul Roetzer +3
Finding correspondences between 3D shapes is an important and long-standing problem in computer vision, graphics and beyond. A prominent challenge are partial-to-partial shape matc…