OpenSU3D: Open World 3D Scene Understanding using Foundation Models

This study presents a novel, scalable approach for constructing open set, instance-level 3D scene representations, advancing open world understanding of 3D environments. Existing methods require pre-constructed 3D scenes and face scalability issues due to per-point feature vector learning, limit- ing their efficacy with complex queries. Our method overcomes these limitations by incrementally building instance-level 3D scene representations using 2D foundation models, efficiently aggregating instance-level details such as masks, feature vectors, names, and captions. We introduce fusion schemes for feature vectors to enhance contextual integration and performance on complex queries. Additionally, we explore large language models for robust automatic annotation and spatial reasoning tasks. Evaluated on scenes from ScanNet [1] and Replica [2] datasets, our method demonstrates zero-shot generalization capabilities, exceeds current state-of-the-art methods in open world 3D scene understanding.