13 citations · 18 across the 3 of their papers we have counts for
16 papers
Improving Multi-View Stereo via Super-Resolution
Eugenio Lomurno, Andrea Romanoni, Matteo Matteucci
Today, Multi-View Stereo techniques are able to reconstruct robust and detailed 3D models, especially when starting from high-resolution images. However, there are cases in which t…
Facetwise Mesh Refinement for Multi-View Stereo
Andrea Romanoni, Matteo Matteucci
Mesh refinement is a fundamental step for accurate Multi-View Stereo. It modifies the geometry of an initial manifold mesh to minimize the photometric error induced in a set of cam…
A Differentiable Recurrent Surface for Asynchronous Event-Based Data
Marco Cannici, Marco Ciccone, Andrea Romanoni +1
Dynamic Vision Sensors (DVSs) asynchronously stream events in correspondence of pixels subject to brightness changes. Differently from classic vision devices, they produce a sparse…
Mesh-based Camera Pairs Selection and Occlusion-Aware Masking for Mesh Refinement
Andrea Romanoni, Matteo Matteucci
Many Multi-View-Stereo algorithms extract a 3D mesh model of a scene, after fusing depth maps into a volumetric representation of the space. Due to the limited scalability of such…
TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo
Andrea Romanoni, Matteo Matteucci
One of the most successful approaches in Multi-View Stereo estimates a depth map and a normal map for each view via PatchMatch-based optimization and fuses them into a consistent 3…
Dense 3D Visual Mapping via Semantic Simplification
Luca Morreale, Andrea Romanoni, Matteo Matteucci
Dense 3D visual mapping estimates as many as possible pixel depths, for each image. This results in very dense point clouds that often contain redundant and noisy information, espe…