  In NeRF-aided editing tasks, object movement presents difficulties in
supervision generation due to the introduction of variability in object
positions. Moreover, the removal operations of certain scene objects often lead
to empty regions, presenting challenges for NeRF models in inpainting them
effectively. We propose an implicit ray transformation strategy, allowing for
direct manipulation of the 3D object's pose by operating on the neural-point in
NeRF rays. To address the challenge of inpainting potential empty regions, we
present a plug-and-play inpainting module, dubbed differentiable neural-point
resampling (DNR), which interpolates those regions in 3D space at the original
ray locations within the implicit space, thereby facilitating object removal &
scene inpainting tasks. Importantly, employing DNR effectively narrows the gap
between ground truth and predicted implicit features, potentially increasing
the mutual information (MI) of the features across rays. Then, we leverage DNR
and ray transformation to construct a point-based editable NeRF pipeline
PR^2T-NeRF. Results primarily evaluated on 3D object removal & inpainting tasks
indicate that our pipeline achieves state-of-the-art performance. In addition,
our pipeline supports high-quality rendering visualization for diverse editing
operations without necessitating extra supervision.
