  We present a novel framework for 3D object-centric representation learning.
Our approach effectively decomposes complex scenes into individual objects from
a single image in an unsupervised fashion. This method, called slot-guided
Volumetric Object Radiance Fields (sVORF), composes volumetric object radiance
fields with object slots as a guidance to implement unsupervised 3D scene
decomposition. Specifically, sVORF obtains object slots from a single image via
a transformer module, maps these slots to volumetric object radiance fields
with a hypernetwork and composes object radiance fields with the guidance of
object slots at a 3D location. Moreover, sVORF significantly reduces memory
requirement due to small-sized pixel rendering during training. We demonstrate
the effectiveness of our approach by showing top results in scene decomposition
and generation tasks of complex synthetic datasets (e.g., Room-Diverse).
Furthermore, we also confirm the potential of sVORF to segment objects in
real-world scenes (e.g., the LLFF dataset). We hope our approach can provide
preliminary understanding of the physical world and help ease future research
in 3D object-centric representation learning.
