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NeuralEditor: Editing Neural Radiance Fields via Manipulating Point Clouds

immortalco.github.io · 376 words · saved by 1 readers

This paper proposes NeuralEditor that enables neural radiance fields (NeRFs) natively editable for general shape editing tasks. Despite their impressive results on novel-view synthesis, it remains a fundamental challenge for NeRFs to edit the shape of the scene. Our key insight is to exploit the explicit point cloud representation as the underlying structure to construct NeRFs, inspired by the intuitive interpretation of NeRF rendering as a process that projects or “plots” the associated 3D point cloud to a 2D image plane. To this end, NeuralEditor introduces a novel rendering scheme based on deterministic integration within K-D tree-guided density-adaptive voxels, which produces both high-quality rendering results and precise point clouds through optimization. NeuralEditor then performs shape editing via mapping associated points between point clouds. Extensive evaluation shows that NeuralEditor achieves state-of-the-art performance in both shape deformation and scene morphing tasks.

NeuralEditor: Editing Neural Radiance Fields via Manipulating Point Clouds NeuralEditor : Editing Neural Radiance Fields via Manipulating Point Clouds CVPR 2023 Jun-Kun Chen 1† Jipeng Lyu 2† Yu-Xiong Wang 1 1 University of Illinois at Urbana-Champaign   2 Peking University   † Equal Contribution { junkun3 , yxw }@illinois.edu   lvjipeng@pku.edu.cn Paper Poster Video Benchmark Code --> Human-Guided Shape Deformation Scene Morphing Abstract --> This paper proposes NeuralEditor that enables neural radiance fields (NeRFs) natively editable for general shape editing tasks. Despite th

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