title: OCTScenes: A Versatile Real-World Dataset of Tabletop Scenes for
    Object-Centric Learning
summary: 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.
paper_authors._id: 648fdda7a92b7b05343280f8
paper_authors.name: Yinxuan Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-09-19T15:54:24.859Z
paper_authors._id: 6401e3373e3d0f2745ad7e1c
paper_authors.avatarUrl: /avatars/3c585883eaa65c3d8f3a5e463bbe8683.svg
paper_authors.fullname: Yinxuan Huang
paper_authors.type: user
paper_authors.user: Yinxuan
paper_authors._id: 648fdda7a92b7b05343280f9
paper_authors.name: Tonglin Chen
paper_authors._id: 648fdda7a92b7b05343280fa
paper_authors.name: Zhimeng Shen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-19T11:44:34.688Z
paper_authors._id: 64903e62877fc29cb1b18261
paper_authors.avatarUrl: /avatars/c4c47722e243445ec73e3a820ec6b9b2.svg
paper_authors.fullname: ZHIMENG SHEN
paper_authors.type: user
paper_authors.user: WTDER
paper_authors._id: 648fdda7a92b7b05343280fb
paper_authors.name: Jinghao Huang
paper_authors._id: 648fdda7a92b7b05343280fc
paper_authors.name: Bin Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-19T13:05:33.980Z
paper_authors._id: 649045a664eea7500c9f0cde
paper_authors.avatarUrl: /avatars/52d576ca8ec1a023c23dc17f9dbc7051.svg
paper_authors.fullname: Bin Li
paper_authors.type: user
paper_authors.user: libin-ai
paper_authors._id: 648fdda7a92b7b05343280fd
paper_authors.name: Xiangyang Xue
paper_title: OCTScenes: A Versatile Real-World Dataset of Tabletop Scenes for
    Object-Centric Learning
paper_summary: 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.
paper_ai_summary: OCTScenes, a comprehensive real-world dataset of tabletop scenes, addresses the challenge of applying object-centric representation learning methods to complex, real-world environments.
paper_ai_keywords: object-centric representation learning OCTScenes tabletop scenes everyday objects 360-degree perspective static scenes dynamic scenes multi-view scenes meaningful representations