# NARF24: Estimating Articulated Object Structure for Implicit Rendering

arXiv: https://arxiv.org/abs/2409.09829 · HTML: https://mlsanity.com/paper/2409.09829/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 8 references that are in the map.
- First posted on arXiv 15 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023)
- [TEASER: Fast and Certifiable Point Cloud Registration](https://mlsanity.com/paper/2001.07715/) (2020)
- [Neural Implicit Representation for Building Digital Twins of Unknown Articulated Objects](https://mlsanity.com/paper/2404.01440/) (2024)
- [A Probabilistic Framework for Learning Kinematic Models of Articulated Objects](https://mlsanity.com/paper/1405.7705/) (2014)
- [Parts-Based Articulated Object Localization in Clutter Using Belief Propagation](https://mlsanity.com/paper/2008.02881/) (2020)
- [NARF22: Neural Articulated Radiance Fields for Configuration-Aware Rendering](https://mlsanity.com/paper/2210.01166/) (2022)

## Built on by (most cited first, 1 of 1)

- [SPLATART: Articulated Gaussian Splatting with Estimated Object Structure](https://mlsanity.com/paper/2506.12184/) (2025)

## Abstract (arXiv)

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.