We study inferring 3D object-centric scene representations from a single
image. While recent methods have shown potential in unsupervised 3D object
discovery from simple synthetic images, they fail to generalize to real-world
scenes with visually rich and diverse objects. This limitation stems from their
object representations, which entangle objects' intrinsic attributes like shape
and appearance with extrinsic, viewer-centric properties such as their 3D
location. To address this bottleneck, we propose Unsupervised discovery of
Object-Centric neural Fields (uOCF). uOCF focuses on learning the intrinsics of
objects and models the extrinsics separately. Our approach significantly
improves systematic generalization, thus enabling unsupervised learning of
high-fidelity object-centric scene representations from sparse real-world
images. To evaluate our approach, we collect three new datasets, including two
real kitchen environments. Extensive experiments show that uOCF enables
unsupervised discovery of visually rich objects from a single real image,
allowing applications such as 3D object segmentation and scene manipulation.
Notably, uOCF demonstrates zero-shot generalization to unseen objects from a
single real image. Project page: this https URL

我们研究了从单一图像中推断 3D 物体中心场景表示的方法，并通过分开建模内外在属性来改善现有的无监督学习方法的局限性，使其能够从稀疏的现实世界图像中无监督地学习高保真的物体中心场景表示。