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arXiv · 2407.18497

Answerability Fields: Answerable Location Estimation via Diffusion Models

Daichi Azuma·Taiki Miyanishi·Shuhei Kurita·Koya Sakamoto·Motoaki Kawanabe
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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.

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Daichi Azuma, Taiki Miyanishi, Shuhei Kurita, Koya Sakamoto, Motoaki Kawanabe. 2024-07-26. Answerability Fields: Answerable Location Estimation via Diffusion Models. https://arxiv.org/abs/2407.18497

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