Answerability Fields: Answerable Location Estimation via Diffusion Models
This work addresses the challenge of enabling machines to better understand and interact with their environments, though it appears incremental as it builds on existing datasets and methods.
The paper tackles the problem of predicting answerability in complex indoor environments by proposing Answerability Fields, which uses a diffusion model to infer answerability from a 3D question answering dataset based on ScanNet. The results demonstrate the efficacy of this approach in guiding scene-understanding tasks.
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.