# Learning Material-Aware Local Descriptors for 3D Shapes

arXiv: https://arxiv.org/abs/1810.08729 · HTML: https://mlsanity.com/paper/1810.08729/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2018.

## Built on by (most cited first, 8 of 8)

- [Where2Act: From Pixels to Actions for Articulated 3D Objects](https://mlsanity.com/paper/2101.02692/) (2021)
- [3DCoMPaT++: An improved Large-scale 3D Vision Dataset for Compositional Recognition](https://mlsanity.com/paper/2310.18511/) (2023)
- [Geometric Deep Learning for Computer-Aided Design: A Survey](https://mlsanity.com/paper/2402.17695/) (2024)
- [AutoMate: A Dataset and Learning Approach for Automatic Mating of CAD Assemblies](https://mlsanity.com/paper/2105.12238/) (2021)
- [SOPHY: Learning to Generate Simulation-Ready Objects with Physical Materials](https://mlsanity.com/paper/2504.12684/) (2025)
- [3DCoMPaT200: Language-Grounded Compositional Understanding of Parts and Materials of 3D Shapes](https://mlsanity.com/paper/2501.06785/) (2025)
- [SLAT-Phys: Fast Material Property Field Prediction from Structured 3D Latents](https://mlsanity.com/paper/2603.23973/) (2026)
- [Adaptive Volumetric Mechanical Property Fields Invariant to Resolution](https://mlsanity.com/paper/2606.18231/) (2026)

## Abstract (arXiv)

Material understanding is critical for design, geometric modeling, and analysis of functional objects. We enable material-aware 3D shape analysis by employing a projective convolutional neural network architecture to learn material- aware descriptors from view-based representations of 3D points for point-wise material classification or material- aware retrieval. Unfortunately, only a small fraction of shapes in 3D repositories are labeled with physical mate- rials, posing a challenge for learning methods. To address this challenge, we crowdsource a dataset of 3080 3D shapes with part-wise material labels. We focus on furniture models which exhibit interesting structure and material variabil- ity. In addition, we also contribute a high-quality expert- labeled benchmark of 115 shapes from Herman-Miller and IKEA for evaluation. We further apply a mesh-aware con- ditional random field, which incorporates rotational and reflective symmetries, to smooth our local material predic- tions across neighboring surface patches. We demonstrate the effectiveness of our learned descriptors for automatic texturing, material-aware retrieval, and physical simulation. The dataset and code will be publicly available.