Cycle consistency has long been exploited as a powerful prior for jointly optimizing maps
within a collection of shapes. In this paper, we investigate its utility in the approaches of …

In this work, we present a novel non-rigid shape matching framework based on multi-
resolution functional maps with spectral attention. Existing functional map learning methods …

We propose a novel learning-based approach for robust 3D shape matching. Our method
builds upon deep functional maps and can be trained in a fully unsupervised manner …

Deep functional maps have recently emerged as a successful paradigm for non-rigid 3D
shape correspondence tasks. An essential step in this pipeline consists in learning feature …

We introduce pointwise map smoothness via the Dirich-let energy into the functional map
pipeline, and propose an algorithm for optimizing it efficiently, which leads to highquality …

In the realm of computer vision and graphics accurately establishing correspondences
between geometric 3D shapes is pivotal for applications like object tracking registration …

In this paper, we introduce complex functional maps, which extend the functional map
framework to conformal maps between tangent vector fields on surfaces. A key property of …

Abstract We present Shape Non-rigid Kinematics (SNK), a novel zero-shot method for non-
rigid shape matching that eliminates the need for extensive training or ground truth data …

In this paper, we present a novel pipeline to compute and refine a data-driven solution for
estimating the correspondence between 3D point clouds. Our method is compatible with the …

To alleviate human labor in redrawing keyframes with ordered vector strokes for automatic
inbetweening, we for the first time propose a joint stroke tracing and correspondence …