  In this work, we aim to detect the changes caused by object variations in a
scene represented by the neural radiance fields (NeRFs). Given an arbitrary
view and two sets of scene images captured at different timestamps, we can
predict the scene changes in that view, which has significant potential
applications in scene monitoring and measuring. We conducted preliminary
studies and found that such an exciting task cannot be easily achieved by
utilizing existing NeRFs and 2D change detection methods with many false or
missing detections. The main reason is that the 2D change detection is based on
the pixel appearance difference between spatial-aligned image pairs and
neglects the stereo information in the NeRF. To address the limitations, we
propose the C-NERF to represent scene changes as directional consistency
difference-based NeRF, which mainly contains three modules. We first perform
the spatial alignment of two NeRFs captured before and after changes. Then, we
identify the change points based on the direction-consistent constraint; that
is, real change points have similar change representations across view
directions, but fake change points do not. Finally, we design the change map
rendering process based on the built NeRFs and can generate the change map of
an arbitrarily specified view direction. To validate the effectiveness, we
build a new dataset containing ten scenes covering diverse scenarios with
different changing objects. Our approach surpasses state-of-the-art 2D change
detection and NeRF-based methods by a significant margin.
