ObPose: Leveraging Pose for Object-Centric Scene Inference and Generation in 3D
This work addresses the problem of learning structured 3D scene representations for robotics and computer vision, though it appears incremental by building on prior 2D and 3D methods.
The authors tackled unsupervised 3D object-centric scene inference and generation by proposing ObPose, which uses pose as an inductive bias to learn factorized latent representations from RGB-D scenes, resulting in significant performance improvements over the state-of-the-art on datasets like YCB, MultiShapeNet, and CLEVR.
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.