Answerability Fields: Answerable Location Estimation via Diffusion Models

TL;DR

This paper introduces Answerability Fields, a diffusion model-based approach for predicting answerability in complex indoor scenes, utilizing a new dataset to improve scene understanding for AI and robotics applications.

Contribution

It presents a novel diffusion model method and a comprehensive dataset for answerability prediction in indoor environments, advancing scene understanding capabilities.

Findings

Answerability Fields effectively guide scene understanding tasks.

The diffusion model accurately infers answerability in diverse scenes.

The dataset enables robust evaluation of answerability prediction methods.

Abstract

In an era characterized by advancements in artificial intelligence and robotics, enabling machines to interact with and understand their environment is a critical research endeavor. In this paper, we propose Answerability Fields, a novel approach to predicting answerability within complex indoor environments. Leveraging a 3D question answering dataset, we construct a comprehensive Answerability Fields dataset, encompassing diverse scenes and questions from ScanNet. Using a diffusion model, we successfully infer and evaluate these Answerability Fields, demonstrating the importance of objects and their locations in answering questions within a scene. Our results showcase the efficacy of Answerability Fields in guiding scene-understanding tasks, laying the foundation for their application in enhancing interactions between intelligent agents and their environments.