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Related Experiment Video

Updated: Dec 11, 2025

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Indoor Scene Change Captioning Based on Multimodality Data.

Yue Qiu1,2, Yutaka Satoh1,2, Ryota Suzuki2

  • 1Graduate School of Science and Technology, University of Tsukuba, Tsukuba 305-8577, Japan.

Sensors (Basel, Switzerland)
|August 27, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a framework for describing indoor scene changes using natural language, utilizing RGB images and point cloud data. The approach enhances scene understanding and anomaly detection in real-world robotic applications.

Keywords:
deep learninghuman-robot interactionimage captioningthree-dimensional (3D) vision

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Area of Science:

  • Computer Vision
  • Robotics
  • Natural Language Processing

Background:

  • Scene change recognition is crucial for applications like anomaly detection.
  • Existing methods often focus on static scenes or lack 3D understanding.
  • Previous 3D scene change captioning used simulated data, limiting real-world applicability.

Purpose of the Study:

  • To develop a framework for describing indoor scene changes using natural language text.
  • To address limitations of existing methods by incorporating 3D structures and real-world data.
  • To generate large-scale indoor scene change caption datasets.

Main Methods:

  • Proposed an end-to-end framework for scene change description.
  • Utilized multiple input modalities: RGB images, depth images, and point cloud data.
  • Generated large-scale indoor scene change caption datasets.

Main Results:

  • Models combining RGB images and point cloud data showed high performance in sentence generation and caption correctness.
  • The framework demonstrated robustness in understanding change types, even in complex datasets.
  • Evaluated model performance across various input modalities and dataset complexities.

Conclusions:

  • The developed datasets and framework advance the field of indoor scene change understanding.
  • Multi-modal input, particularly RGB and point cloud data, is effective for scene change captioning.
  • The approach is suitable for real-world robotic applications requiring scene understanding.