OCTScenes: A Versatile Real-World Dataset of Tabletop Scenes for
  Object-Centric Learning
Authors: name: Yinxuan Huang, name: Tonglin Chen, name: Zhimeng Shen, name: Jinghao Huang, name: Bin Li, name: Xiangyang Xue

Humans possess the cognitive ability to comprehend scenes in a compositional
manner. To empower AI systems with similar abilities, object-centric
representation learning aims to acquire representations of individual objects
from visual scenes without any supervision. Although recent advancements in
object-centric representation learning have achieved remarkable progress on
complex synthesis datasets, there is a huge challenge for application in
complex real-world scenes. One of the essential reasons is the scarcity of
real-world datasets specifically tailored to object-centric representation
learning methods. To solve 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 representation learning methods. OCTScenes contains
5000 tabletop scenes with a total of 15 everyday 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 representation learning methods across static
scenes, dynamic scenes, and multi-view scenes tasks. Extensive experiments of
object-centric representation learning methods for static, dynamic and
multi-view scenes 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 serves as a catalyst for
advancing existing state-of-the-art methods, inspiring them to adapt to
real-world scenes. Dataset and code are available at
https://huggingface.co/datasets/Yinxuan/OCTScenes.