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Gianmarco Addari

dblp:242/0842 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0003-2644-7516ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
3D vision · 100%
Computer graphics and multimedia
1 paper
Geometric modeling and processing · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
3d reconstruction
0.712023
A Family of Approaches for Full 3D Reconstruction of Objects with Complex Surface Reflectance · Int. J. Comput. Vis. 2023
Computer vision › 3D vision › stereo vision
helmholtz stereopsis
0.712023
A Family of Approaches for Full 3D Reconstruction of Objects with Complex Surface Reflectance · Int. J. Comput. Vis. 2023

Methods — techniques the papers use, named apart from their topics

volumetric reconstruction · 1.3mesh optimization · 1.3helmholtz reciprocity · 1.3
YearPublicationVenuePosition
2023 A Family of Approaches for Full 3D Reconstruction of Objects with Complex Surface Reflectance
abstract
Abstract 3D reconstruction of general scenes remains an open challenge with current techniques often reliant on assumptions on the scene’s surface reflectance, which restrict the range of objects that can be modelled. Helmholtz Stereopsis offers an appealing framework to make the modelling process agnostic to surface reflectance. However, previous formulations have been almost exclusively limited to 2.5D modelling. To address this gap, this paper introduces a family of reconstruction approaches that exploit Helmholtz reciprocity to produce complete 3D models of objects with arbitrary unknown reflectance. This includes an approach based on the fusion of (orthographic or perspective) view-dependent reconstructions, a volumetric approach optimising surface location within a voxel grid, and a mesh-based formulation optimising vertices positions of a given mesh topology. The contributed approaches are evaluated on synthetic and real datasets, including novel full 3D datasets publicly released with this paper, with experimental comparison against a wide range of competing methods. Results demonstrate the benefits of the different approaches and their abilities to achieve high quality full 3D reconstructions of complex objects.
Gianmarco Addari, Jean-Yves Guillemaut
Int. J. Comput. Vis.1