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 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.
@inproceedings{tang2024nerfdeformer,
title={NeRFDeformer: NeRF Transformation from a Single View via 3D Scene Flows},
author={Tang, Zhenggang and Ren, Zhongzheng and Zhao, Xiaoming and Wen, Bowen and Tremblay, Jonathan and Birchfield, Stan and Schwing, Alexander},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={10293--10303},
year={2024}
}