Non‐rigid registration computes an alignment between a source surface with a target
surface in a non‐rigid manner. In the past decade, with the advances in 3D sensing …

Abstract We present Lepard, a Learning based approach for partial point cloud matching in
rigid and deformable scenes. The key characteristics are the following techniques that …

We introduce a new general-purpose approach to deep learning on three-dimensional
surfaces based on the insight that a simple diffusion layer is highly effective for spatial …

Landslides are catastrophic natural hazards that often lead to loss of life, property damage,
and economic disruption. Image-based landslide investigations are crucial for determining …

We address the problem of fitting 3D human models to 3D scans of dressed humans.
Classical methods optimize both the data-to-model correspondences and the human model …

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 …

We study the problem of outlier correspondence pruning for non-rigid point cloud
registration. In rigid registration, spatial consistency has been a commonly used criterion to …

We consider the problem of computing dense correspondences between non-rigid shapes
with potentially significant partiality. Existing formulations tackle this problem through heavy …

Motivated by the intuition that one can transform two aligned point clouds to each other more
easily and meaningfully than a misaligned pair, we propose CorrNet3D-the first …

In this paper, we propose a transformer-based procedure for the efficient registration of non-
rigid 3D point clouds. The proposed approach is data-driven and adopts for the first time the …