# Exploiting Local Geometry for Feature and Graph Construction for Better 3D Point Cloud Processing with Graph Neural Networks

arXiv: https://arxiv.org/abs/2103.15226 · HTML: https://mlsanity.com/paper/2103.15226/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 46 references that are in the map.
- First posted on arXiv 28 Mar 2021.

## Builds on (most cited first, 30 of 30)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [PointCNN: Convolution On X-Transformed Points](https://mlsanity.com/paper/1801.07791/) (2018)
- [DeepGCNs: Can GCNs Go as Deep as CNNs?](https://mlsanity.com/paper/1904.03751/) (2019)
- [Relation-Shape Convolutional Neural Network for Point Cloud Analysis](https://mlsanity.com/paper/1904.07601/) (2019)
- [PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling](https://mlsanity.com/paper/2003.00492/) (2020)
- [Point Convolutional Neural Networks by Extension Operators](https://mlsanity.com/paper/1803.10091/) (2018)
- [Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models](https://mlsanity.com/paper/1704.01222/) (2017)
- [3D Point Capsule Networks](https://mlsanity.com/paper/1812.10775/) (2018)
- [Virtual Multi-view Fusion for 3D Semantic Segmentation](https://mlsanity.com/paper/2007.13138/) (2020)
- [A Closer Look at Local Aggregation Operators in Point Cloud Analysis](https://mlsanity.com/paper/2007.01294/) (2020)
- [Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification](https://mlsanity.com/paper/1712.00032/) (2017)
- [ConvPoint: Continuous Convolutions for Point Cloud Processing](https://mlsanity.com/paper/1904.02375/) (2019)
- [JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds](https://mlsanity.com/paper/2007.06888/) (2020)
- [Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds](https://mlsanity.com/paper/1909.09287/) (2019)
- [Learning Compact Geometric Features](https://mlsanity.com/paper/1709.05056/) (2017)
- [DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes](https://mlsanity.com/paper/2004.01002/) (2020)
- [Geometry Sharing Network for 3D Point Cloud Classification and Segmentation](https://mlsanity.com/paper/1912.10644/) (2019)
- [Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features](https://mlsanity.com/paper/1904.10014/) (2019)
- [Learning to Segment 3D Point Clouds in 2D Image Space](https://mlsanity.com/paper/2003.05593/) (2020)
- [Beyond Image to Depth: Improving Depth Prediction using Echoes](https://mlsanity.com/paper/2103.08468/) (2021)
- [LP-3DCNN: Unveiling Local Phase in 3D Convolutional Neural Networks](https://mlsanity.com/paper/1904.03498/) (2019)
- [Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds](https://mlsanity.com/paper/1907.12046/) (2019)
- [DeepPoint3D: Learning Discriminative Local Descriptors using Deep Metric Learning on 3D Point Clouds](https://mlsanity.com/paper/1904.00817/) (2019)
- [3D Binary Signatures](https://mlsanity.com/paper/1708.07937/) (2017)

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

- [OmniVec: Learning robust representations with cross modal sharing](https://mlsanity.com/paper/2311.05709/) (2023)
- [Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformers](https://mlsanity.com/paper/2404.09411/) (2024)
- [Probing Equivariance and Symmetry Breaking in Convolutional Networks](https://mlsanity.com/paper/2501.01999/) (2025)
- [Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification](https://mlsanity.com/paper/2108.06317/) (2021)
- [ClusterViG: Efficient Globally Aware Vision GNNs via Image Partitioning](https://mlsanity.com/paper/2501.10640/) (2025)
- [Group Multi-View Transformer for 3D Shape Analysis with Spatial Encoding](https://mlsanity.com/paper/2312.16477/) (2023)
- [GSTran: Joint Geometric and Semantic Coherence for Point Cloud Segmentation](https://mlsanity.com/paper/2408.11558/) (2024)
- [A Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation](https://mlsanity.com/paper/2605.17131/) (2026)
- [GPT-based Textile Pilling Classification Using 3D Point Cloud Data](https://mlsanity.com/paper/2408.10496/) (2024)

## Abstract (arXiv)

We propose simple yet effective improvements in point representations and local neighborhood graph construction within the general framework of graph neural networks (GNNs) for 3D point cloud processing. As a first contribution, we propose to augment the vertex representations with important local geometric information of the points, followed by nonlinear projection using a MLP. As a second contribution, we propose to improve the graph construction for GNNs for 3D point clouds. The existing methods work with a k-nn based approach for constructing the local neighborhood graph. We argue that it might lead to reduction in coverage in case of dense sampling by sensors in some regions of the scene. The proposed methods aims to counter such problems and improve coverage in such cases. As the traditional GNNs were designed to work with general graphs, where vertices may have no geometric interpretations, we see both our proposals as augmenting the general graphs to incorporate the geometric nature of 3D point clouds. While being simple, we demonstrate with multiple challenging benchmarks, with relatively clean CAD models, as well as with real world noisy scans, that the proposed method achieves state of the art results on benchmarks for 3D classification (ModelNet40) , part segmentation (ShapeNet) and semantic segmentation (Stanford 3D Indoor Scenes Dataset). We also show that the proposed network achieves faster training convergence, i.e. ~40% less epochs for classification. The project details are available at https://siddharthsrivastava.github.io/publication/geomgcnn/