SeamlessNeRF: Stitching Part NeRFs with Gradient Propagation
Bingchen Gong Yuehao Wang Xiaoguang Han Qi Dou
Neural Radiance Fields (NeRFs) have emerged as promising digital mediums of
3D objects and scenes, sparking a surge in research to extend the editing
capabilities in this domain. The task of seamless editing and merging of
multiple NeRFs, resembling the ``Poisson blending'' in 2D image editing,
remains a critical operation that is under-explored by existing work. To fill
this gap, we propose SeamlessNeRF, a novel approach for seamless appearance
blending of multiple NeRFs. In specific, we aim to optimize the appearance of a
target radiance field in order to harmonize its merge with a source field. We
propose a well-tailored optimization procedure for blending, which is
constrained by 1) pinning the radiance color in the intersecting boundary area
between the source and target fields and 2) maintaining the original gradient
of the target. Extensive experiments validate that our approach can effectively
propagate the source appearance from the boundary area to the entire target
field through the gradients. To the best of our knowledge, SeamlessNeRF is the
first work that introduces gradient-guided appearance editing to radiance
fields, offering solutions for seamless stitching of 3D objects represented in
NeRFs.