InternScenes: A Large-scale Simulatable Indoor Scene Dataset with
  Realistic Layouts
The advancement of Embodied AI heavily relies on large-scale, simulatable 3D
scene datasets characterized by scene diversity and realistic layouts. However,
existing datasets typically suffer from limitations in data scale or diversity,
sanitized layouts lacking small items, and severe object collisions. To address
these shortcomings, we introduce InternScenes, a novel large-scale
simulatable indoor scene dataset comprising approximately 40,000 diverse scenes
by integrating three disparate scene sources, real-world scans, procedurally
generated scenes, and designer-created scenes, including 1.96M 3D objects and
covering 15 common scene types and 288 object classes. We particularly preserve
massive small items in the scenes, resulting in realistic and complex layouts
with an average of 41.5 objects per region. Our comprehensive data processing
pipeline ensures simulatability by creating real-to-sim replicas for real-world
scans, enhances interactivity by incorporating interactive objects into these
scenes, and resolves object collisions by physical simulations. We demonstrate
the value of InternScenes with two benchmark applications: scene layout
generation and point-goal navigation. Both show the new challenges posed by the
complex and realistic layouts. More importantly, InternScenes paves the way for
scaling up the model training for both tasks, making the generation and
navigation in such complex scenes possible. We commit to open-sourcing the
data, models, and benchmarks to benefit the whole community.
68c8d095733e345e52ac1e97
Weipeng Zhong
68c8d095733e345e52ac1e98
Peizhou Cao
68c8d095733e345e52ac1e99
Yichen Jin
68c8d095733e345e52ac1e9a
Li Luo
68c8d095733e345e52ac1e9b
Wenzhe Cai
68c8d095733e345e52ac1e9c
Jingli Lin
68c8d095733e345e52ac1e9d
Hanqing Wang
68c8d095733e345e52ac1e9e
Zhaoyang Lyu
68c8d095733e345e52ac1e9f
Tai Wang
68c8d095733e345e52ac1ea0
Bo Dai
68c8d095733e345e52ac1ea1
Xudong Xu
68c8d095733e345e52ac1ea2
Jiangmiao Pang
InternScenes: A Large-scale Simulatable Indoor Scene Dataset with
  Realistic Layouts
The advancement of Embodied AI heavily relies on large-scale, simulatable 3D
scene datasets characterized by scene diversity and realistic layouts. However,
existing datasets typically suffer from limitations in data scale or diversity,
sanitized layouts lacking small items, and severe object collisions. To address
these shortcomings, we introduce InternScenes, a novel large-scale
simulatable indoor scene dataset comprising approximately 40,000 diverse scenes
by integrating three disparate scene sources, real-world scans, procedurally
generated scenes, and designer-created scenes, including 1.96M 3D objects and
covering 15 common scene types and 288 object classes. We particularly preserve
massive small items in the scenes, resulting in realistic and complex layouts
with an average of 41.5 objects per region. Our comprehensive data processing
pipeline ensures simulatability by creating real-to-sim replicas for real-world
scans, enhances interactivity by incorporating interactive objects into these
scenes, and resolves object collisions by physical simulations. We demonstrate
the value of InternScenes with two benchmark applications: scene layout
generation and point-goal navigation. Both show the new challenges posed by the
complex and realistic layouts. More importantly, InternScenes paves the way for
scaling up the model training for both tasks, making the generation and
navigation in such complex scenes possible. We commit to open-sourcing the
data, models, and benchmarks to benefit the whole community.
InternScenes is a large-scale, diverse, and realistic indoor scene dataset that addresses limitations in existing datasets, enabling better scene layout generation and point-goal navigation.
Embodied AI 3D scene datasets scene diversity realistic layouts real-world scans procedurally generated scenes designer-created scenes 3D objects scene types object classes small items real-to-sim replicas interactive objects physical simulations scene layout generation point-goal navigation