6 citations · 12 across the 4 of their papers we have counts for
5 papers
DINE: Distance Is Not Enough -- Learning Global Deformation Priors for Robust Soft-Tissue Point Cloud Registration
Sara Monji-Azad, Rohit Beer, Marvin Kinz +2
Non-rigid point cloud registration is central to soft-tissue shape analysis, but large deformations, noise, and outliers make correspondence estimation challenging. Most learning-b…
Frequency-Structured Field Learning for Light-Field Disparity Estimation
Sara Monji-Azad, Yulin Liu, Jürgen Hesser
Light-field disparity estimation requires global consistency in smooth or textureless regions and local precision near occlusion boundaries, thin structures, and abrupt depth trans…
DefTransNet: A Transformer-based Method for Non-Rigid Point Cloud Registration in the Simulation of Soft Tissue Deformation
Sara Monji-Azad, Marvin Kinz, Siddharth Kothari +5
Soft-tissue surgeries, such as tumor resections, are complicated by tissue deformations that can obscure the accurate location and shape of tissues. By representing tissue surfaces…
SynBench: A Synthetic Benchmark for Non-rigid 3D Point Cloud Registration
Sara Monji-Azad, Marvin Kinz, Claudia Scherl +3
Non-rigid point cloud registration is a crucial task in computer vision. Evaluating a non-rigid point cloud registration method requires a dataset with challenges such as large def…
Robust-DefReg: A Robust Deformable Point Cloud Registration Method based on Graph Convolutional Neural Networks
Sara Monji-Azad, Marvin Kinz, Jürgen Hesser
Point cloud registration is a fundamental problem in computer vision that aims to estimate the transformation between corresponding sets of points. Non-rigid registration, in parti…