3 citations · 8 across the 8 of their papers we have counts for
6 papers · 1 filter
Wormhole Loss for Partial Shape Matching
Amit Bracha, Thomas Dagès, Ron Kimmel
When matching parts of a surface to its whole, a fundamental question arises: Which points should be included in the matching process? The issue is intensified when using isometry…
GS2Mesh: Surface Reconstruction from Gaussian Splatting via Novel Stereo Views
Yaniv Wolf, Amit Bracha, Ron Kimmel
Recently, 3D Gaussian Splatting (3DGS) has emerged as an efficient approach for accurately representing scenes. However, despite its superior novel view synthesis capabilities, ext…
On Unsupervised Partial Shape Correspondence
Amit Bracha, Thomas Dagès, Ron Kimmel
While dealing with matching shapes to their parts, we often apply a tool known as functional maps. The idea is to translate the shape matching problem into "convenient" spaces by w…
Partial Shape Similarity via Alignment of Multi-Metric Hamiltonian Spectra
David Bensaïd, Amit Bracha, Ron Kimmel
Evaluating the similarity of non-rigid shapes with significant partiality is a fundamental task in numerous computer vision applications. Here, we propose a novel axiomatic method…
Depth Refinement for Improved Stereo Reconstruction
Amit Bracha, Noam Rotstein, David Bensaïd +2
Depth estimation is a cornerstone of a vast number of applications requiring 3D assessment of the environment, such as robotics, augmented reality, and autonomous driving to name a…
Multimodal Colored Point Cloud to Image Alignment
Noam Rotstein, Amit Bracha, Ron Kimmel
Reconstruction of geometric structures from images using supervised learning suffers from limited available amount of accurate data. One type of such data is accurate real-world RG…