  We present radiance field propagation (RFP), a novel approach to segmenting
objects in 3D during reconstruction given only unlabeled multi-view images of a
scene. RFP is derived from emerging neural radiance field-based techniques,
which jointly encodes semantics with appearance and geometry. The core of our
method is a novel propagation strategy for individual objects' radiance fields
with a bidirectional photometric loss, enabling an unsupervised partitioning of
a scene into salient or meaningful regions corresponding to different object
instances. To better handle complex scenes with multiple objects and
occlusions, we further propose an iterative expectation-maximization algorithm
to refine object masks. RFP is one of the first unsupervised approach for
tackling 3D real scene object segmentation for neural radiance field (NeRF)
without any supervision, annotations, or other cues such as 3D bounding boxes
and prior knowledge of object class. Experiments demonstrate that RFP achieves
feasible segmentation results that are more accurate than previous unsupervised
image/scene segmentation approaches, and are comparable to existing supervised
NeRF-based methods. The segmented object representations enable individual 3D
object editing operations.
