Place-NeRFs
Year: 2025
Type: article
Abstract: We present the Place-NeRFs, a scalable approach to large-scale 3D scene reconstruction that subdivides scenes into non-overlapping regions that can be handled by off-the-shelf NeRF models, striking a balance between reconstruction quality and efficient use of computational resources. By leveraging rough single-view depth estimation and visibility graphs, Place-NeRFs effectively groups spatially correlated photospheres, enabling independent volumetric reconstructions. This approach significantly reduces processing time and enhances scalability during NeRF models' training. Experiments on large-scale industrial scenarios, including sparse, complex, and non-uniform spread of views, showcase the efficiency of this method in the face of diverse challenges.
Source: IEEE SIGPORT
DOI: 10.60864/a3cy-pc49
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6921588724
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