5 citations · 8 across the 7 of their papers we have counts for
9 papers
Model-based Metric 3D Shape and Motion Reconstruction of Wild Bottlenose Dolphins in Drone-Shot Videos
Daniele Baieri, Riccardo Cicciarella, Michael Krützen +2
We address the problem of estimating the metric 3D shape and motion of wild dolphins from monocular video, with the aim of assessing their body condition. While considerable progre…
SShaDe: scalable shape deformation via local representations
Filippo Maggioli, Daniele Baieri, Zorah Lähner +1
With the increase in computational power for the available hardware, the demand for high-resolution data in computer graphics applications increases. Consequently, classical geomet…
: Cycle-Consistent Multi-Model Merging
Donato Crisostomi, Marco Fumero, Daniele Baieri +2
In this paper, we present a novel data-free method for merging neural networks in weight space. Differently from most existing works, our method optimizes for the permutations of n…
Implicit-ARAP: Efficient Handle-Guided Neural Field Deformation via Local Patch Meshing
Daniele Baieri, Filippo Maggioli, Emanuele Rodolà +2
Neural fields have emerged as a powerful representation for 3D geometry, enabling compact and continuous modeling of complex shapes. Despite their expressive power, manipulating ne…
GSEdit: Efficient Text-Guided Editing of 3D Objects via Gaussian Splatting
Francesco Palandra, Andrea Sanchietti, Daniele Baieri +1
We present GSEdit, a pipeline for text-guided 3D object editing based on Gaussian Splatting models. Our method enables the editing of the style and appearance of 3D objects without…
Efficient Generation of Multimodal Fluid Simulation Data
Daniele Baieri, Donato Crisostomi, Stefano Esposito +2
In this work, we introduce an efficient generation procedure to produce synthetic multi-modal datasets of fluid simulations. The procedure can reproduce the dynamics of fluid flows…
ReMatching: Low-Resolution Representations for Scalable Shape Correspondence
Filippo Maggioli, Daniele Baieri, Emanuele Rodolà +1
We introduce \emph{ReMatching}, a novel shape correspondence solution based on the functional maps framework. Our method, by exploiting a new and appropriate \emph{re}-meshing para…
Fluid Dynamics Network: Topology-Agnostic 4D Reconstruction via Fluid Dynamics Priors
Daniele Baieri, Stefano Esposito, Filippo Maggioli +1
Representing 3D surfaces as level sets of continuous functions over is the common denominator of neural implicit representations, which recently enabled remarkable p…
KiloNeuS: A Versatile Neural Implicit Surface Representation for Real-Time Rendering
Stefano Esposito, Daniele Baieri, Stefan Zellmann +2
NeRF-based techniques fit wide and deep multi-layer perceptrons (MLPs) to a continuous radiance field that can be rendered from any unseen viewpoint. However, the lack of surface a…