InternScenes: A Large-scale Simulatable Indoor Scene Dataset with
  Realistic Layouts
Authors: name: Weipeng Zhong, name: Peizhou Cao, name: Yichen Jin, name: Li Luo, name: Wenzhe Cai, name: Jingli Lin, name: Hanqing Wang, name: Zhaoyang Lyu, name: Tai Wang, name: Bo Dai, name: Xudong Xu, name: Jiangmiao Pang

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.