Answerability Fields: Answerable Location Estimation via Diffusion Models
<|reference_start|>Answerability Fields: Answerable Location Estimation via Diffusion Models: 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.<|reference_end|>
@article{azuma2024answerability,
    title={Answerability Fields: Answerable Location Estimation via Diffusion
  Models},
    author={Daichi Azuma, Taiki Miyanishi, Shuhei Kurita, Koya Sakamoto, Motoaki
  Kawanabe},
    journal={arXiv preprint arXiv:2407.18497},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18497},
    primaryClass={cs.CV}
}