A toolbox for generating multidimensional 3D objects with fine-controlled feature space: Quaddle 2.0

Area of Science:

• Computer Vision
• Human-Computer Interaction
• Cognitive Science

Background:

• Generating and controlling multidimensional 3D-rendered objects is crucial for vision research and gaming.
• Existing methods face challenges in parametric control and efficient object generation.

Purpose of the Study:

• To present a novel Python toolbox for the parametric control and efficient generation of multidimensional 3D-rendered objects.
• To enable fine-tuning of object features and control over object similarity for research applications.

Main Methods:

• Developed a toolbox defining objects as multidimensional feature vectors with adjustable dimensions (primary, secondary, accessory).
• Integrated the toolbox with Blender for object creation and Python scripting for fine-tuning.
• Utilized the gaming engine Unity to embed generated objects in a 3D-rendered augmented reality environment for a sequence learning task.

Main Results:

• The toolbox allows for gradual morphing of features across multiple dimensions.
• It enables precise control over feature similarity between generated objects.
• Automated generation of multiple objects in 3D and 2D formats is supported.

Conclusions:

• The toolbox provides efficient generation of multidimensional objects with fine-grained feature control.
• It facilitates the creation of objects with controllable similarity, beneficial for visual cognitive research.
• The system is suitable for developing immersive visual environments and augmented reality applications.