NARF24: Estimating Articulated Object Structure for Implicit Rendering

• URL: http://arxiv.org/abs/2409.09829v1
• Date: Sun, 15 Sep 2024 19:06:46 GMT
• Title: NARF24: Estimating Articulated Object Structure for Implicit Rendering
• Authors: Stanley Lewis, Tom Gao, Odest Chadwicke Jenkins,
• Abstract summary: 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.
• Score: 8.044069980286812
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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