Table of Links Abstract and 1 Introduction Abstract and 1 Introduction Related Works
2.1. Vision-and-Language Navigation
2.2. Semantic Scene Understanding and Instance Segmentation
2.3. 3D Scene Reconstruction


Methodology
3.1. Data Collection
3.2. Open-set Semantic Information from Images
3.3. Creating the Open-set 3D Representation
3.4. Language-Guided Navigation


Experiments
4.1. Quantitative Evaluation
4.2. Qualitative Results


Conclusion and Future Work, Disclosure statement, and References Related Works
2.1. Vision-and-Language Navigation
2.2. Semantic Scene Understanding and Instance Segmentation
2.3. 3D Scene Reconstruction Related Works 2.1. Vision-and-Language Navigation 2.1. Vision-and-Language Navigation 2.2. Semantic Scene Understanding and Instance Segmentation 2.2. Semantic Scene Understanding and Instance Segmentation 2.3. 3D Scene Reconstruction 2.3. 3D Scene Reconstruction Methodology
3.1. Data Collection
3.2. Open-set Semantic Information from Images
3.3. Creating the Open-set 3D Representation
3.4. Language-Guided Navigation Methodology Methodology 3.1. Data Collection 3.1. Data Collection 3.2. Open-set Semantic Information from Images 3.2. Open-set Semantic Information from Images 3.3. Creating the Open-set 3D Representation 3.3. Creating the Open-set 3D Representation 3.4. Language-Guided Navigation 3.4. Language-Guided Navigation Experiments
4.1. Quantitative Evaluation
4.2. Qualitative Results Experiments Experiments 4.1. Quantitative Evaluation 4.1. Quantitative Evaluation 4.2. Qualitative Results 4.2. Qualitative Results Conclusion and Future Work, Disclosure statement, and References Conclusion and Future Work, Disclosure statement, and References Conclusion and Future Work, Disclosure statement, and References 4. Experiments Having introduced the O3D-SIM creation pipeline and its integration with ChatGPT for natural language understanding and Vision-Language Navigation (VLN) enhancement, we now turn to the evaluation of this novel representation both quantitatively and qualitatively. This will also shed light on the impact of the O3D-SIM representation on an agent’s ability to execute queries that mimic human interaction. The evaluation is structured into two subsections: Section 4.1 focuses on the quantitative assessment of O3D-SIM, and Section 4.2 addresses the qualitative analysis of the representation. Authors:
(1) Laksh Nanwani, International Institute of Information Technology, Hyderabad, India; this author contributed equally to this work;
(2) Kumaraditya Gupta, International Institute of Information Technology, Hyderabad, India;
(3) Aditya Mathur, International Institute of Information Technology, Hyderabad, India; this author contributed equally to this work;
(4) Swayam Agrawal, International Institute of Information Technology, Hyderabad, India;
(5) A.H. Abdul Hafez, Hasan Kalyoncu University, Sahinbey, Gaziantep, Turkey;
(6) K. Madhava Krishna, International Institute of Information Technology, Hyderabad, India. Authors: Authors: (1) Laksh Nanwani, International Institute of Information Technology, Hyderabad, India; this author contributed equally to this work; (2) Kumaraditya Gupta, International Institute of Information Technology, Hyderabad, India; (3) Aditya Mathur, International Institute of Information Technology, Hyderabad, India; this author contributed equally to this work; (4) Swayam Agrawal, International Institute of Information Technology, Hyderabad, India; (5) A.H. Abdul Hafez, Hasan Kalyoncu University, Sahinbey, Gaziantep, Turkey; (6) K. Madhava Krishna, International Institute of Information Technology, Hyderabad, India. This paper is available on arxiv under CC by-SA 4.0 Deed (Attribution-Sharealike 4.0 International) license. This paper is available on arxiv under CC by-SA 4.0 Deed (Attribution-Sharealike 4.0 International) license. available on arxiv available on arxiv

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Evaluating Novel 3D Semantic Instance Map for Vision-Language Navigation

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