A Robust 3D-2D Interactive Tool for Scene Segmentation and Annotation.

Area of Science:

• Computer Vision
• 3D Data Processing
• Geometric Modeling

Background:

• Large-scale 3D scene data acquisition is advancing rapidly.
• Annotating 3D data is crucial for computer vision and graphics but remains challenging due to complexity and lack of effective tools.
• Existing methods struggle with clutter and varying illumination in 3D scenes.

Purpose of the Study:

• To develop a robust and user-friendly annotation tool for segmenting and annotating massive 3D data.
• To couple 2D and 3D information within an interactive framework for semantic annotation.
• To facilitate the creation of large-scale annotated 3D datasets.

Main Methods:

• Developed an interactive annotation tool integrating 2D and 3D scene information.
• Implemented a framework allowing users to provide high-level semantic annotations for objects.
• Utilized the tool to annotate a dataset of over one hundred 3D scenes.

Main Results:

• The developed tool significantly reduces 3D scene annotation time, from hours to under 30 minutes for typical indoor scenes.
• Achieved effective segmentation and annotation of complex 3D data.
• Created a comprehensive dataset of annotated 3D scenes.

Conclusions:

• The interactive tool offers an efficient solution for annotating large-scale 3D data.
• The tool and accompanying dataset will accelerate research in computer vision and graphics, particularly in areas like scene understanding and object recognition.
• The approach demonstrates the potential of coupled 2D/3D information for robust 3D data annotation.