  Humans possess the cognitive ability to comprehend scenes in a compositional
manner. To empower AI systems with similar capabilities, object-centric
learning aims to acquire representations of individual objects from visual
scenes without any supervision. Although recent advances in object-centric
learning have made remarkable progress on complex synthesis datasets, there is
a huge challenge for application to complex real-world scenes. One of the
essential reasons is the scarcity of real-world datasets specifically tailored
to object-centric learning. To address this problem, we propose a versatile
real-world dataset of tabletop scenes for object-centric learning called
OCTScenes, which is meticulously designed to serve as a benchmark for
comparing, evaluating, and analyzing object-centric learning methods. OCTScenes
contains 5000 tabletop scenes with a total of 15 objects. Each scene is
captured in 60 frames covering a 360-degree perspective. Consequently,
OCTScenes is a versatile benchmark dataset that can simultaneously satisfy the
evaluation of object-centric learning methods based on single-image, video, and
multi-view. Extensive experiments of representative object-centric learning
methods are conducted on OCTScenes. The results demonstrate the shortcomings of
state-of-the-art methods for learning meaningful representations from
real-world data, despite their impressive performance on complex synthesis
datasets. Furthermore, OCTScenes can serve as a catalyst for the advancement of
existing methods, inspiring them to adapt to real-world scenes. Dataset and
code are available at https://huggingface.co/datasets/Yinxuan/OCTScenes.
