  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.
