Open-Set 3D Semantic Instance Maps for Vision Language Navigation -- O3D-SIM

• URL: http://arxiv.org/abs/2404.17922v1
• Date: Sat, 27 Apr 2024 14:20:46 GMT
• Title: Open-Set 3D Semantic Instance Maps for Vision Language Navigation -- O3D-SIM
• Authors: Laksh Nanwani, Kumaraditya Gupta, Aditya Mathur, Swayam Agrawal, A. H. Abdul Hafez, K. Madhava Krishna,
• Abstract summary: Humans excel at forming mental maps of their surroundings. Having instance-level information and the semantic understanding of an environment helps significantly improve performance for language-guided tasks. We extend this instance-level approach to 3D while increasing the pipeline's robustness.
• Score: 8.46789360111679
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Humans excel at forming mental maps of their surroundings, equipping them to understand object relationships and navigate based on language queries. Our previous work SI Maps [1] showed that having instance-level information and the semantic understanding of an environment helps significantly improve performance for language-guided tasks. We extend this instance-level approach to 3D while increasing the pipeline's robustness and improving quantitative and qualitative results. Our method leverages foundational models for object recognition, image segmentation, and feature extraction. We propose a representation that results in a 3D point cloud map with instance-level embeddings, which bring in the semantic understanding that natural language commands can query. Quantitatively, the work improves upon the success rate of language-guided tasks. At the same time, we qualitatively observe the ability to identify instances more clearly and leverage the foundational models and language and image-aligned embeddings to identify objects that, otherwise, a closed-set approach wouldn't be able to identify.

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