# Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification

arXiv: https://arxiv.org/abs/2207.10278 · HTML: https://mlsanity.com/paper/2207.10278/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 70 references that are in the map.
- First posted on arXiv 21 Jul 2022.

## Builds on (most cited first, 30 of 31)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [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)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [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)
- [PointConv: Deep Convolutional Networks on 3D Point Clouds](https://mlsanity.com/paper/1811.07246/) (2018)
- [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://mlsanity.com/paper/1911.11236/) (2019)
- [Multi-view Convolutional Neural Networks for 3D Shape Recognition](https://mlsanity.com/paper/1505.00880/) (2015)
- [DeepGCNs: Can GCNs Go as Deep as CNNs?](https://mlsanity.com/paper/1904.03751/) (2019)
- [Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs](https://mlsanity.com/paper/1711.09869/) (2017)
- [SEGCloud: Semantic Segmentation of 3D Point Clouds](https://mlsanity.com/paper/1710.07563/) (2017)
- [Tangent Convolutions for Dense Prediction in 3D](https://mlsanity.com/paper/1807.02443/) (2018)
- [Deep Parametric Continuous Convolutional Neural Networks](https://mlsanity.com/paper/2101.06742/) (2021)
- [Point Convolutional Neural Networks by Extension Operators](https://mlsanity.com/paper/1803.10091/) (2018)
- [DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing](https://mlsanity.com/paper/1909.03669/) (2019)
- [A Closer Look at Local Aggregation Operators in Point Cloud Analysis](https://mlsanity.com/paper/2007.01294/) (2020)
- [VV-Net: Voxel VAE Net with Group Convolutions for Point Cloud Segmentation](https://mlsanity.com/paper/1811.04337/) (2018)
- [PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation](https://mlsanity.com/paper/1807.00652/) (2018)
- [Semantic Stereo for Incidental Satellite Images](https://mlsanity.com/paper/1811.08739/) (2018)
- [Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks](https://mlsanity.com/paper/1302.1700/) (2013)
- [GA-NET: Global Attention Network for Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2107.03101/) (2021)
- [PyramNet: Point Cloud Pyramid Attention Network and Graph Embedding Module for Classification and Segmentation](https://mlsanity.com/paper/1906.03299/) (2019)
- [Directionally Constrained Fully Convolutional Neural Network For Airborne Lidar Point Cloud Classification](https://mlsanity.com/paper/1908.06673/) (2019)
- [Addressing Overfitting on Pointcloud Classification using Atrous XCRF](https://mlsanity.com/paper/1902.03088/) (2019)
- [A Fully Convolutional Network for Semantic Labeling of 3D Point Clouds](https://mlsanity.com/paper/1710.01408/) (2017)
- [Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification](https://mlsanity.com/paper/1910.05909/) (2019)

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

- [Bidirectional Feature Globalization for Few-shot Semantic Segmentation of 3D Point Cloud Scenes](https://mlsanity.com/paper/2208.06671/) (2022)
- [Elevation Estimation-Driven Building 3D Reconstruction from Single-View Remote Sensing Imagery](https://mlsanity.com/paper/2301.04581/) (2023)
- [Breaking Immutable: Information-Coupled Prototype Elaboration for Few-Shot Object Detection](https://mlsanity.com/paper/2211.14782/) (2022)
- [Not Just Learning from Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensing](https://mlsanity.com/paper/2310.12452/) (2023)
- [Prompt-and-Transfer: Dynamic Class-aware Enhancement for Few-shot Segmentation](https://mlsanity.com/paper/2409.10389/) (2024)
- [Enhancing Monocular Height Estimation via Sparse LiDAR-Guided Correction](https://mlsanity.com/paper/2505.06905/) (2025)
- [BoundED: Neural Boundary and Edge Detection in 3D Point Clouds via Local Neighborhood Statistics](https://mlsanity.com/paper/2210.13305/) (2022)
- [SDL-MVS: View Space and Depth Deformable Learning Paradigm for Multi-View Stereo Reconstruction in Remote Sensing](https://mlsanity.com/paper/2405.17140/) (2024)
- [Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models](https://mlsanity.com/paper/2311.13200/) (2023)
- [Aerial Lifting: Neural Urban Semantic and Building Instance Lifting from Aerial Imagery](https://mlsanity.com/paper/2403.11812/) (2024)
- [TAFormer: A Unified Target-Aware Transformer for Video and Motion Joint Prediction in Aerial Scenes](https://mlsanity.com/paper/2403.18238/) (2024)
- [SFTformer: A Spatial-Frequency-Temporal Correlation-Decoupling Transformer for Radar Echo Extrapolation](https://mlsanity.com/paper/2402.18044/) (2024)
- [CMAB: A First National-Scale Multi-Attribute Building Dataset in China Derived from Open Source Data and GeoAI](https://mlsanity.com/paper/2408.05891/) (2024)
- [An Enhanced Classification Method Based on Adaptive Multi-Scale Fusion for Long-tailed Multispectral Point Clouds](https://mlsanity.com/paper/2412.11407/) (2024)
- [Twin Deformable Point Convolutions for Point Cloud Semantic Segmentation in Remote Sensing Scenes](https://mlsanity.com/paper/2405.19735/) (2024)
- [An Enhanced Geometric-Spectral Feature Learning Framework for Airborne Multispectral Point Cloud Classification](https://mlsanity.com/paper/2606.09123/) (2026)

## Abstract (arXiv)

The classification of airborne laser scanning (ALS) point clouds is a critical task of remote sensing and photogrammetry fields. Although recent deep learning-based methods have achieved satisfactory performance, they have ignored the unicity of the receptive field, which makes the ALS point cloud classification remain challenging for the distinguishment of the areas with complex structures and extreme scale variations. In this article, for the objective of configuring multi-receptive field features, we propose a novel receptive field fusion-and-stratification network (RFFS-Net). With a novel dilated graph convolution (DGConv) and its extension annular dilated convolution (ADConv) as basic building blocks, the receptive field fusion process is implemented with the dilated and annular graph fusion (DAGFusion) module, which obtains multi-receptive field feature representation through capturing dilated and annular graphs with various receptive regions. The stratification of the receptive fields with point sets of different resolutions as the calculation bases is performed with Multi-level Decoders nested in RFFS-Net and driven by the multi-level receptive field aggregation loss (MRFALoss) to drive the network to learn in the direction of the supervision labels with different resolutions. With receptive field fusion-and-stratification, RFFS-Net is more adaptable to the classification of regions with complex structures and extreme scale variations in large-scale ALS point clouds. Evaluated on the ISPRS Vaihingen 3D dataset, our RFFS-Net significantly outperforms the baseline approach by 5.3% on mF1 and 5.4% on mIoU, accomplishing an overall accuracy of 82.1%, an mF1 of 71.6%, and an mIoU of 58.2%. Furthermore, experiments on the LASDU dataset and the 2019 IEEE-GRSS Data Fusion Contest dataset show that RFFS-Net achieves a new state-of-the-art classification performance.