Semantic-guided compositional scene representation framework.

Area of Science:

• Computer Vision
• Computer Graphics
• Machine Learning

Background:

• Neural scene representation methods are crucial but struggle with generalization to new, unseen scenes.
• Existing methods face challenges in representing invisible or novel environments effectively.

Purpose of the Study:

• To develop a novel semantic-guided compositional scene representation framework.
• To enhance the generalization capabilities of neural scene representations for invisible scenes.
• To improve training efficiency for multi-object scene representations.

Main Methods:

• A framework comprising a baseline scene representation module, a semantic mapping module, and a compositional representation strategy was developed.
• The semantic mapping module learns embedding correlations between explicit semantic attributes and implicit scene representations.
• A compositional strategy fuses single-object representations into multi-object representations for improved efficiency.

Main Results:

• The proposed framework successfully represents invisible scenes using only semantic attributes.
• The compositional strategy significantly improves training efficiency for multi-object scenes.
• Experiments on three datasets confirm high-accuracy representations for both visible and invisible multi-object scenes.

Conclusions:

• The semantic-guided compositional framework offers a robust solution for generalizing neural scene representations to unseen environments.
• This approach enhances the applicability of neural scene representations in complex, real-world scenarios.
• The method demonstrates improved accuracy and training efficiency for multi-object scene representation.