  We present ObPose, an unsupervised object-centric inference and generation
model which learns 3D-structured latent representations from RGB-D scenes.
Inspired by prior art in 2D representation learning, ObPose considers a
factorised latent space, separately encoding object location (where) and
appearance (what). ObPose further leverages an object's pose (i.e. location and
orientation), defined via a minimum volume principle, as a novel inductive bias
for learning the where component. To achieve this, we propose an efficient,
voxelised approximation approach to recover the object shape directly from a
neural radiance field (NeRF). As a consequence, ObPose models each scene as a
composition of NeRFs, richly representing individual objects. To evaluate the
quality of the learned representations, ObPose is evaluated quantitatively on
the YCB, MultiShapeNet, and CLEVR datatasets for unsupervised scene
segmentation, outperforming the current state-of-the-art in 3D scene inference
(ObSuRF) by a significant margin. Generative results provide qualitative
demonstration that the same ObPose model can both generate novel scenes and
flexibly edit the objects in them. These capacities again reflect the quality
of the learned latents and the benefits of disentangling the where and what
components of a scene. Key design choices made in the ObPose encoder are
validated with ablations.
