5 papers
SBP-Net: Learning Thin Structure Reconstruction with Sliding-Box Projections
Ofir Gilad, Andrei Sharf
Reconstructing thin 3D structures is challenging due to their sparsity, scale variation, and complex geometry. Such structures arise in a wide range of domains, including medical i…
Stochastic Porous Microstructures
Zhongren Wang, Lihao Tian, Xiaokang Liu +2
Stochastic porous structures are ubiquitous in natural phenomena and have gained considerable traction across diverse domains owing to their exceptional physical properties. The re…
VNT-Net: Rotational Invariant Vector Neuron Transformers
Hedi Zisling, Andrei Sharf
Learning 3D point sets with rotational invariance is an important and challenging problem in machine learning. Through rotational invariant architectures, 3D point cloud neural net…
Multi-modal 3D Shape Reconstruction Under Calibration Uncertainty using Parametric Level Set Methods
Moshe Eliasof, Andrei Sharf, Eran Treister
We consider the problem of 3D shape reconstruction from multi-modal data, given uncertain calibration parameters. Typically, 3D data modalities can be in diverse forms such as spar…
Printed Perforated Lampshades for Continuous Projective Images
Haisen Zhao, Lin Lu, Yuan Wei +4
We present a technique for designing 3D-printed perforated lampshades, which project continuous grayscale images onto the surrounding walls. Given the geometry of the lampshade and…