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 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.

ObPose 是一个无监督的对象中心推理生成模型，可以从 RGB-D 场景中学习三维结构的潜在表示，并通过将对象位置和外观分开编码来获得卓越的性能。