mlsanityData to 7 Oct 2026

Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scene

arXiv 2301.12972 ↗First posted 30 Jan 2023cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

This paper proposes EyeNet, a novel semantic segmentation network for point clouds that addresses the critical yet often overlooked parameter of coverage area size. Inspired by human peripheral vision, EyeNet overcomes the limitations of conventional networks by introducing a simple but efficient multi-contour input and a parallel processing network with connection blocks between parallel streams. The proposed approach effectively addresses the challenges of dense point clouds, as demonstrated by our ablation studies and state-of-the-art performance on Large-Scale Outdoor datasets.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 22 of its 40 references that are in the map. First posted on arXiv 30 Jan 2023.

Builds on

22 of 40 references.

References · 22

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2017Squeeze-and-Excitation Networkscited by 4,763
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  4. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  5. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,202
  6. 20173D Semantic Segmentation with Submanifold Sparse Convolutional Networkscited by 746
  7. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 584
  8. 2017Large-scale Point Cloud Semantic Segmentation with Superpoint Graphscited by 384
  9. 20203D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detectioncited by 176
  10. 2018Tangent Convolutions for Dense Prediction in 3Dcited by 173
  11. 2020EPNet: Enhancing Point Features with Image Semantics for 3D Object Detectioncited by 164
  12. 2017Semantic3D.net: A new Large-scale Point Cloud Classification Benchmarkcited by 158
10 more
  1. 2020Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challengescited by 80
  2. 2020Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadwayscited by 77
  3. 2020DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentationcited by 65
  4. 2019ConvPoint: Continuous Convolutions for Point Cloud Processingcited by 62
  5. 2022Peripheral Vision Transformercited by 27
  6. 2021SUM: A Benchmark Dataset of Semantic Urban Meshescited by 23
  7. 2020Emergent Properties of Foveated Perceptual Systemscited by 22
  8. 2021Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoningcited by 22
  9. 2020FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modellingcited by 17
  10. 2017SideEye: A Generative Neural Network Based Simulator of Human Peripheral Visioncited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 2.

4 papers.

  1. 2024An Enhanced Classification Method Based on Adaptive Multi-Scale Fusion for Long-tailed Multispectral Point Cloudscited by 1
  2. 2023APNet: Urban-level Scene Segmentation of Aerial Images and Point Cloudscited by 0
  3. 2026From Spherical to Gaussian: A Comparative Analysis of Point Cloud Cropping Strategies in Large-Scale 3D Environmentscited by 0
  4. 2026An Enhanced Geometric-Spectral Feature Learning Framework for Airborne Multispectral Point Cloud Classificationcited by 0