Time-Series Household Indoor Data

Dataset Overview

Background

Most existing indoor datasets (e.g., Matterport3D, ScanNet, Replica) only provide static, one-time scans of household environments. However, in the real world, robots and intelligent systems operate in dynamic households, where objects shift, lighting changes, and obstacles appear or disappear every day. Currently, academia and industry lack long-term, continuous, and realistic household datasets that capture these temporal dynamics, limiting progress in embodied AI, robotics, and smart home research.

Core Features of the Dataset

  • Low-level perspective (10–40 cm): Relevant for service and cleaning robots.
  • Dynamic changes: Subtle differences in object placement, lighting, and furniture.
  • Temporal continuity: Daily updates capturing natural variations.
  • Realistic household settings: Authentic, non-synthetic environments.

Potential Applications

Embodied AI & Robot Training

  • Supports incremental SLAM and dynamic environment navigation.
  • Enables cross-day task planning.
  • Improves robots' robustness to environmental changes.

Long-term 3D Modeling & Smart Homes

  • Builds time-evolving NeRF/3D reconstructions.
  • Supports health monitoring and layout optimization.
  • Analyzes furniture usage and movement patterns.

Cleaning & Service Robot Optimization

  • Identifies high-frequency obstruction zones.
  • Enables predictive obstacle avoidance.

Behavior & Habit Modeling

  • Infers household activity from object/furniture dynamics.
  • Enables personalized smart home services.

Market & Research Value

  • Academic value: Fills the gap of time-series indoor datasets, rare in today's research.
  • Long-term potential: Scalable to multi-household, multi-region, multi-season deployments.
  • Industrial value: Training material for robots, cleaning devices, and smart homes.

Vision: Building a global embodied intelligence data asset through time-series household datasets.

This dataset transforms household environments from static snapshots into continuous environmental stories. It accelerates research breakthroughs while delivering practical training data for robotics and smart home industries, paving the way for robust embodied intelligence.