60 citations · 79 across the 10 of their papers we have counts for
6 papers · 1 filter
Deep Confidence Guided Distance for 3D Partial Shape Registration
Dvir Ginzburg, Dan Raviv
We present a novel non-iterative learnable method for partial-to-partial 3D shape registration. The partial alignment task is extremely complex, as it jointly tries to match betwee…
DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction
Itai Lang, Dvir Ginzburg, Shai Avidan +1
We present a new method for real-time non-rigid dense correspondence between point clouds based on structured shape construction. Our method, termed Deep Point Correspondence (DPC)…
Deep Weighted Consensus: Dense correspondence confidence maps for 3D shape registration
Dvir Ginzburg, Dan Raviv
We present a new paradigm for rigid alignment between point clouds based on learnable weighted consensus which is robust to noise as well as the full spectrum of the rotation group…
Unsupervised Scale-Invariant Multispectral Shape Matching
Idan Pazi, Dvir Ginzburg, Dan Raviv
Alignment between non-rigid stretchable structures is one of the most challenging tasks in computer vision, as the invariant properties are hard to define, and there is no labeled…
Dual Geometric Graph Network (DG2N) -- Iterative network for deformable shape alignment
Dvir Ginzburg, Dan Raviv
We provide a novel new approach for aligning geometric models using a dual graph structure where local features are mapping probabilities. Alignment of non-rigid structures is one…
Cyclic Functional Mapping: Self-supervised correspondence between non-isometric deformable shapes
Dvir Ginzburg, Dan Raviv
We present the first utterly self-supervised network for dense correspondence mapping between non-isometric shapes. The task of alignment in non-Euclidean domains is one of the mos…