# Towards Part-Based Understanding of RGB-D Scans

arXiv: https://arxiv.org/abs/2012.02094 · HTML: https://mlsanity.com/paper/2012.02094/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2020.

## Built on by (most cited first, 9 of 9)

- [PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models](https://mlsanity.com/paper/2212.01558/) (2022)
- [PartSLIP++: Enhancing Low-Shot 3D Part Segmentation via Multi-View Instance Segmentation and Maximum Likelihood Estimation](https://mlsanity.com/paper/2312.03015/) (2023)
- [ANISE: Assembly-based Neural Implicit Surface rEconstruction](https://mlsanity.com/paper/2205.13682/) (2022)
- [Neural Rendering in a Room: Amodal 3D Understanding and Free-Viewpoint Rendering for the Closed Scene Composed of Pre-Captured Objects](https://mlsanity.com/paper/2205.02714/) (2022)
- [Neural Part Priors: Learning to Optimize Part-Based Object Completion in RGB-D Scans](https://mlsanity.com/paper/2203.09375/) (2022)
- [ExCap3D: Expressive 3D Scene Understanding via Object Captioning with Varying Detail](https://mlsanity.com/paper/2503.17044/) (2025)
- [Scan2Part: Fine-grained and Hierarchical Part-level Understanding of Real-World 3D Scans](https://mlsanity.com/paper/2206.02366/) (2022)
- [Shape Anchor Guided Holistic Indoor Scene Understanding](https://mlsanity.com/paper/2309.11133/) (2023)
- [PinPoint3D: Fine-Grained 3D Part Segmentation from a Few Clicks](https://mlsanity.com/paper/2509.25970/) (2025)

## Abstract (arXiv)

Recent advances in 3D semantic scene understanding have shown impressive progress in 3D instance segmentation, enabling object-level reasoning about 3D scenes; however, a finer-grained understanding is required to enable interactions with objects and their functional understanding. Thus, we propose the task of part-based scene understanding of real-world 3D environments: from an RGB-D scan of a scene, we detect objects, and for each object predict its decomposition into geometric part masks, which composed together form the complete geometry of the observed object. We leverage an intermediary part graph representation to enable robust completion as well as building of part priors, which we use to construct the final part mask predictions. Our experiments demonstrate that guiding part understanding through part graph to part prior-based predictions significantly outperforms alternative approaches to the task of semantic part completion.