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