Deep scene-scale material estimation from multi-view indoor captures

Prakash, Siddhant; Rainer, Gilles; Bousseau, Adrien; Drettakis, George

doi:10.1016/j.cag.2022.09.010

Cited by 4 publications

(1 citation statement)

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“…We used 5 scenes, Lego from NeRF Synthetic dataset, XmasBalls 1 , Kitchen from [Prakash et al 2022], as well as two real-world scenes (Hallway and Statues) that we captured ourselves. We apply different and diverse deformations and edits for all scenes, both synthetic and captured, focusing on affine and non-affine transformations and object duplication.…”

Section: Resultsmentioning

confidence: 99%

NeRFshop

Jambon

Kerbl

Kopanas

et al. 2023

Proc. ACM Comput. Graph. Interact. Tech.

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Neural Radiance Fields (NeRFs) have revolutionized novel view synthesis for captured scenes, with recent methods allowing interactive free-viewpoint navigation and fast training for scene reconstruction. However, the implicit representations used by these methods---often including neural networks and complex encodings---make them difficult to edit. Some initial methods have been proposed, but they suffer from limited editing capabilities and/or from a lack of interactivity, and are thus unsuitable for interactive editing of captured scenes. We tackle both limitations and introduce NeRFshop, a novel end-to-end method that allows users to interactively select and deform objects through cage-based transformations. NeRFshop provides fine scribble-based user control for the selection of regions or objects to edit, semi-automatic cage creation, and interactive volumetric manipulation of scene content thanks to our GPU-friendly two-level interpolation scheme. Further, we introduce a preliminary approach that reduces potential resulting artifacts of these transformations with a volumetric membrane interpolation technique inspired by Poisson image editing and provide a process that "distills" the edits into a standalone NeRF representation.

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Section: Resultsmentioning

confidence: 99%