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Volumetric Environment Representation for Vision-Language Navigation
March 22, 2024, 4:45 a.m. | Rui Liu, Wenguan Wang, Yi Yang
cs.CV updates on arXiv.org arxiv.org
Abstract: Vision-language navigation (VLN) requires an agent to navigate through an 3D environment based on visual observations and natural language instructions. It is clear that the pivotal factor for successful navigation lies in the comprehensive scene understanding. Previous VLN agents employ monocular frameworks to extract 2D features of perspective views directly. Though straightforward, they struggle for capturing 3D geometry and semantics, leading to a partial and incomplete environment representation. To achieve a comprehensive 3D representation with …
abstract agent agents arxiv clear cs.cv environment extract features frameworks language lies natural natural language navigation perspective pivotal representation through type understanding vision visual
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Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs
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