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Papers

NARF24: Estimating Articulated Object Structure for Implicit Rendering

2024-09-15 · Stanley Lewis, Tom Gao, Odest Chadwicke Jenkins

Articulated objects and their representations pose a difficult problem for robots. These objects require not only representations of geometry and texture, but also of the various connections and joint parameters that make up each articulation. We propose a method that learns a common Neural Radiance Field (NeRF) representation across a small number of collected scenes. This representation is combined with a parts-based image segmentation to produce an implicit space part localization, from which the connectivity and joint parameters of the articulated object can be estimated, thus enabling configuration-conditioned rendering.

📄 PDF Abstract BibTeX arXiv:2409.09829

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Tasks

Image SegmentationNeRFObjectSemantic Segmentation

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