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.
@article{arxiv.2407.18497,
title = {Answerability Fields: Answerable Location Estimation via Diffusion Models},
author = {Daichi Azuma and Taiki Miyanishi and Shuhei Kurita and Koya Sakamoto and Motoaki Kawanabe},
journal= {arXiv preprint arXiv:2407.18497},
year = {2024}
}