The One Where They Reconstructed 3D Humans and Environments in TV Shows

Pavlakos, Georgios; Weber, Ethan; Tancik, Matthew; Kanazawa, Angjoo

doi:10.48550/arxiv.2207.14279

Cited by 1 publication

(1 citation statement)

References 62 publications

(94 reference statements)

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“…The enrichment of NeRFs with semantic information has not been developed yet for cultural heritage: Zhi et al (2021) extended NeRFs scene-specific representation to include semantic representations that were efficiently learned from partial sparse or noisy annotations of indoor scenes. Similarly, Pavlakos et al (2022) relied on NeRF models for an accurate estimation of human pose and location. The recovered, semantically enriched 3D scene context was used to render novel views of the human localization within certain environments.…”

Section: Semantic Nerfsmentioning

confidence: 99%

Neural Radiance Fields (Nerf): Review and Potential Applications to Digital Cultural Heritage

Croce

Crepaldi

Luca

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Neural Radiance Fields (NeRF or NeRFs) are to date emerging as a novel method for synthesizing novel views of complex 3D scenes, leveraging an artificial neural network to optimize a volumetric scene function using a set of input views. We conduct a preliminary critical review of the scientific and technical literature on NeRFs, and we highlight possible applications of the latter in the Cultural Heritage domain, for the image-based reconstruction of 3D models of real, multi-scale objects, even in combination with the more well-established photogrammetric techniques. A comparison is made between NeRFs and photogrammetry in terms of operating procedures and outputs (volumetric renderings vs. point clouds or meshes). It is demonstrated that NeRFs could be conveniently used for rendering objects (sculptures, archaeological remains, sites, paintings etc.) that are challenging for photogrammetry, typically: i) metallic, translucent, and/or transparent surfaces; ii) objects that present homogeneous textures; iii) occlusions, vegetation, and elements of very fine detail.

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Section: Semantic Nerfsmentioning

confidence: 99%