# Parsing Geometry Using Structure-Aware Shape Templates

arXiv: https://arxiv.org/abs/1808.01337 · HTML: https://mlsanity.com/paper/1808.01337/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2018.

## Built on by (most cited first, 16 of 16)

- [Joint Learning of 3D Shape Retrieval and Deformation](https://mlsanity.com/paper/2101.07889/) (2021)
- [Point Primitive Transformer for Long-Term 4D Point Cloud Video Understanding](https://mlsanity.com/paper/2208.00281/) (2022)
- [Img2CAD: Reverse Engineering 3D CAD Models from Images through VLM-Assisted Conditional Factorization](https://mlsanity.com/paper/2408.01437/) (2024)
- [U-RED: Unsupervised 3D Shape Retrieval and Deformation for Partial Point Clouds](https://mlsanity.com/paper/2308.06383/) (2023)
- [SRIF: Semantic Shape Registration Empowered by Diffusion-based Image Morphing and Flow Estimation](https://mlsanity.com/paper/2409.11682/) (2024)
- [ShapeMatcher: Self-Supervised Joint Shape Canonicalization, Segmentation, Retrieval and Deformation](https://mlsanity.com/paper/2311.11106/) (2023)
- [Spice-E : Structural Priors in 3D Diffusion using Cross-Entity Attention](https://mlsanity.com/paper/2311.17834/) (2023)
- [KP-RED: Exploiting Semantic Keypoints for Joint 3D Shape Retrieval and Deformation](https://mlsanity.com/paper/2403.10099/) (2024)
- [Fit4CAD: A point cloud benchmark for fitting simple geometric primitives in CAD objects](https://mlsanity.com/paper/2105.06858/) (2021)
- [Semantic-Aware Implicit Template Learning via Part Deformation Consistency](https://mlsanity.com/paper/2308.11916/) (2023)
- [GenAnalysis: Joint Shape Analysis by Learning Man-Made Shape Generators with Deformation Regularizations](https://mlsanity.com/paper/2503.00807/) (2025)
- [Seg&Struct: The Interplay Between Part Segmentation and Structure Inference for 3D Shape Parsing](https://mlsanity.com/paper/2211.00382/) (2022)
- [Fabrication-Aware Reverse Engineering for Carpentry](https://mlsanity.com/paper/2107.09965/) (2021)
- [Unsupervised Representation Learning for Diverse Deformable Shape Collections](https://mlsanity.com/paper/2310.18141/) (2023)
- [Parameterize Structure with Differentiable Template for 3D Shape Generation](https://mlsanity.com/paper/2410.10399/) (2024)
- [3DMiner: Discovering Shapes from Large-Scale Unannotated Image Datasets](https://mlsanity.com/paper/2310.19188/) (2023)

## Abstract (arXiv)

Real-life man-made objects often exhibit strong and easily-identifiable structure, as a direct result of their design or their intended functionality. Structure typically appears in the form of individual parts and their arrangement. Knowing about object structure can be an important cue for object recognition and scene understanding - a key goal for various AR and robotics applications. However, commodity RGB-D sensors used in these scenarios only produce raw, unorganized point clouds, without structural information about the captured scene. Moreover, the generated data is commonly partial and susceptible to artifacts and noise, which makes inferring the structure of scanned objects challenging. In this paper, we organize large shape collections into parameterized shape templates to capture the underlying structure of the objects. The templates allow us to transfer the structural information onto new objects and incomplete scans. We employ a deep neural network that matches the partial scan with one of the shape templates, then match and fit it to complete and detailed models from the collection. This allows us to faithfully label its parts and to guide the reconstruction of the scanned object. We showcase the effectiveness of our method by comparing it to other state-of-the-art approaches.