3D rendering techniques have undergone a rapid evolution with the emergence of novel and advanced methodologies, redefining the boundaries of realism and computational efficiency. This study explores recent advancements in the field, comparing established approaches like photogrammetry with software such as COLMAP against the new frontiers opened by emerging view synthesis approaches like Neural Radiance Fields (NeRF), and 3D Gaussian Splatting. In this paper, we present a comprehensive comparison of the described methods tailored for industrial applications, where the data acquisition is generally conducted by human operators employing handheld devices.

3D reconstruction methods in industrial settings: a comparative study for COLMAP, NeRF and 3D Gaussian Splatting / Sambugaro, Z.; Orlandi, L.; Conci, N.. - 3762:(2024), pp. 212-217. (Intervento presentato al convegno 2024 Ital-IA Intelligenza Artificiale - Thematic Workshops, Ital-IA 2024 tenutosi a ita nel 2024).

3D reconstruction methods in industrial settings: a comparative study for COLMAP, NeRF and 3D Gaussian Splatting

Sambugaro Z.;Orlandi L.;Conci N.
2024-01-01

Abstract

3D rendering techniques have undergone a rapid evolution with the emergence of novel and advanced methodologies, redefining the boundaries of realism and computational efficiency. This study explores recent advancements in the field, comparing established approaches like photogrammetry with software such as COLMAP against the new frontiers opened by emerging view synthesis approaches like Neural Radiance Fields (NeRF), and 3D Gaussian Splatting. In this paper, we present a comprehensive comparison of the described methods tailored for industrial applications, where the data acquisition is generally conducted by human operators employing handheld devices.
2024
CEUR Workshop Proceedings
Germany
CEUR-WS
Sambugaro, Z.; Orlandi, L.; Conci, N.
3D reconstruction methods in industrial settings: a comparative study for COLMAP, NeRF and 3D Gaussian Splatting / Sambugaro, Z.; Orlandi, L.; Conci, N.. - 3762:(2024), pp. 212-217. (Intervento presentato al convegno 2024 Ital-IA Intelligenza Artificiale - Thematic Workshops, Ital-IA 2024 tenutosi a ita nel 2024).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/436827
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