Lightplane: Highly-Scalable Components for Neural 3D Fields

• URL: http://arxiv.org/abs/2404.19760v1
• Date: Tue, 30 Apr 2024 17:59:51 GMT
• Title: Lightplane: Highly-Scalable Components for Neural 3D Fields
• Authors: Ang Cao, Justin Johnson, Andrea Vedaldi, David Novotny,
• Abstract summary: Lightplane Render and Splatter significantly reduce memory usage in 2D-3D mapping. These innovations enable the processing of vastly more and higher resolution images with small memory and computational costs.
• Score: 54.59244949629677
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contemporary 3D research, particularly in reconstruction and generation, heavily relies on 2D images for inputs or supervision. However, current designs for these 2D-3D mapping are memory-intensive, posing a significant bottleneck for existing methods and hindering new applications. In response, we propose a pair of highly scalable components for 3D neural fields: Lightplane Render and Splatter, which significantly reduce memory usage in 2D-3D mapping. These innovations enable the processing of vastly more and higher resolution images with small memory and computational costs. We demonstrate their utility in various applications, from benefiting single-scene optimization with image-level losses to realizing a versatile pipeline for dramatically scaling 3D reconstruction and generation. Code: \url{https://github.com/facebookresearch/lightplane}.

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