Answerability Fields: Answerable Location Estimation via Diffusion Models
- URL: http://arxiv.org/abs/2407.18497v1
- Date: Fri, 26 Jul 2024 04:02:46 GMT
- Title: Answerability Fields: Answerable Location Estimation via Diffusion Models
- Authors: Daichi Azuma, Taiki Miyanishi, Shuhei Kurita, Koya Sakamoto, Motoaki Kawanabe,
- Abstract summary: We propose Answerability Fields, a novel approach to predicting answerability within complex indoor environments.
Our results showcase the efficacy of Answerability Fields in guiding scene-understanding tasks.
- Score: 9.234108543963568
- License: http://creativecommons.org/licenses/by/4.0/
- 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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