8 citations · 10 across the 5 of their papers we have counts for
8 papers
Learning Incompressible 3D Flows from Sparse Samples: A Set Transformer Approach to Divergence-Free Kernel Fitting
Ramzi Idoughi, Radouan Boukharfane, Peter Wonka
Recovering divergence-free velocity fields from sparse and corrupted observations requires reconciling physical consistency, expressive representations, and robustness to missing d…
Lightweight Neural Framework for Robust 3D Volume and Surface Estimation from Multi-View Images
Diego E. Farchione, Ramzi Idoughi, Peter Wonka
Accurate volume and surface area estimation is critical for diverse applications, from marine ecology to medical diagnostics. However, existing methods often suffer from high compu…
Any Resolution Any Geometry: From Multi-View To Multi-Patch
Wenqing Cui, Zhenyu Li, Mykola Lavreniuk +4
Joint estimation of surface normals and depth is essential for holistic 3D scene understanding, yet high-resolution prediction remains difficult due to the trade-off between preser…
No Mesh, No Problem: Estimating Coral Volume and Surface from Sparse Multi-View Images
Diego Eustachio Farchione, Ramzi Idoughi, Peter Wonka
Effective reef monitoring requires the quantification of coral growth via accurate volumetric and surface area estimates, which is a challenging task due to the complex morphology…
DissolveStereo: Coarse Depth Injection for Zero-Shot Stereo Video Generation
Jian Shi, Qian Wang, Zhenyu Li +3
Generating high-quality stereo videos requires consistent depth perception and temporal coherence across frames. Despite advances in image and video synthesis using diffusion model…
HDNet: Physics-Inspired Neural Network for Flow Estimation based on Helmholtz Decomposition
Miao Qi, Ramzi Idoughi, Wolfgang Heidrich
Flow estimation problems are ubiquitous in scientific imaging. Often, the underlying flows are subject to physical constraints that can be exploited in the flow estimation; for exa…
Neural Adaptive SCEne Tracing
Rui Li, Darius Rückert, Yuanhao Wang +2
Neural rendering with implicit neural networks has recently emerged as an attractive proposition for scene reconstruction, achieving excellent quality albeit at high computational…
NeAT: Neural Adaptive Tomography
Darius Rückert, Yuanhao Wang, Rui Li +2
In this paper, we present Neural Adaptive Tomography (NeAT), the first adaptive, hierarchical neural rendering pipeline for multi-view inverse rendering. Through a combination of n…