NeRFDeformer: NeRF Transformation from a Single View via 3D Scene Flows

• URL: http://arxiv.org/abs/2406.10543v1
• Date: Sat, 15 Jun 2024 07:58:08 GMT
• Title: NeRFDeformer: NeRF Transformation from a Single View via 3D Scene Flows
• Authors: Zhenggang Tang, Zhongzheng Ren, Xiaoming Zhao, Bowen Wen, Jonathan Tremblay, Stan Birchfield, Alexander Schwing,
• Abstract summary: We present a method for automatically modifying a NeRF representation based on a single observation. Our method defines the transformation as a 3D flow, specifically as a weighted linear blending of rigid transformations. We also introduce a new dataset for exploring the problem of modifying a NeRF scene through a single observation.
• Score: 60.291277312569285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a method for automatically modifying a NeRF representation based on a single observation of a non-rigid transformed version of the original scene. Our method defines the transformation as a 3D flow, specifically as a weighted linear blending of rigid transformations of 3D anchor points that are defined on the surface of the scene. In order to identify anchor points, we introduce a novel correspondence algorithm that first matches RGB-based pairs, then leverages multi-view information and 3D reprojection to robustly filter false positives in two steps. We also introduce a new dataset for exploring the problem of modifying a NeRF scene through a single observation. Our dataset ( https://github.com/nerfdeformer/nerfdeformer ) contains 113 synthetic scenes leveraging 47 3D assets. We show that our proposed method outperforms NeRF editing methods as well as diffusion-based methods, and we also explore different methods for filtering correspondences.

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