SeamlessNeRF: Stitching Part NeRFs with Gradient Propagation
This addresses a critical under-explored operation for 3D editing in NeRFs, offering a novel solution for seamless stitching, though it appears incremental as it adapts 2D blending concepts to 3D.
The paper tackles the problem of seamlessly editing and merging multiple Neural Radiance Fields (NeRFs) for 3D objects and scenes, proposing SeamlessNeRF, which optimizes appearance blending by propagating gradients from boundary areas, effectively harmonizing merges as validated in experiments.
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.