mlsanityData to 7 Oct 2026

PillarSegNet: Pillar-based Semantic Grid Map Estimation using Sparse LiDAR Data

arXiv 2105.04169 ↗First posted 10 May 2021cs.CV cs.ROcited by 16

Not on the map. Why?

Abstract (arXiv)

Semantic understanding of the surrounding environment is essential for automated vehicles. The recent publication of the SemanticKITTI dataset stimulates the research on semantic segmentation of LiDAR point clouds in urban scenarios. While most existing approaches predict sparse pointwise semantic classes for the sparse input LiDAR scan, we propose PillarSegNet to be able to output a dense semantic grid map. In contrast to a previously proposed grid map method, PillarSegNet uses PointNet to learn features directly from the 3D point cloud and then conducts 2D semantic segmentation in the top view. To train and evaluate our approach, we use both sparse and dense ground truth, where the dense ground truth is obtained from multiple superimposed scans. Experimental results on the SemanticKITTI dataset show that PillarSegNet achieves a performance gain of about 10% mIoU over the state-of-the-art grid map method.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 14 of its 21 references that are in the map. First posted on arXiv 10 May 2021.

Builds on

14 of 21 references.

References · 14

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  4. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  5. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  6. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  7. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  8. 2019What You See is What You Get: Exploiting Visibility for 3D Object Detectioncited by 69
  9. 2020Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platformcited by 40
  10. 2020Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Studycited by 33
  11. 2020SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentationcited by 13
  12. 2018Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networkscited by 6
2 more
  1. 2019Motion Estimation in Occupancy Grid Maps in Stationary Settings Using Recurrent Neural Networkscited by 6
  2. 2020Exploiting Multi-Layer Grid Maps for Surround-View Semantic Segmentation of Sparse LiDAR Datacited by 5

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 2; 2023: 6; 2024: 3; 2025: 3.

16 papers.

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  1. 2023PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye Viewcited by 24
  2. 2021MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understandingcited by 22
  3. 2023Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehiclescited by 9
  4. 2025AGO: Adaptive Grounding for Open World 3D Occupancy Predictioncited by 9
  5. 2023Safe Navigation in Unstructured Environments by Minimizing Uncertainty in Control and Perceptioncited by 5
  6. 2024UFO: Uncertainty-aware LiDAR-image Fusion for Off-road Semantic Terrain Map Estimationcited by 5
  7. 2021Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian Inferencecited by 4
  8. 2025HiLoTs: High-Low Temporal Sensitive Representation Learning for Semi-Supervised LiDAR Segmentation in Autonomous Drivingcited by 3
  9. 2023LAPTNet-FPN: Multi-scale LiDAR-aided Projective Transform Network for Real Time Semantic Grid Predictioncited by 2
  10. 2022Circular Accessible Depth: A Robust Traversability Representation for UGV Navigationcited by 1
  11. 2023Semantic Segmentation on 3D Point Clouds with High Density Variationscited by 1
  12. 2024Hierarchical Insights: Exploiting Structural Similarities for Reliable 3D Semantic Segmentationcited by 1
4 more
  1. 2022LAPTNet: LiDAR-Aided Perspective Transform Networkcited by 0
  2. 2023METAVerse: Meta-Learning Traversability Cost Map for Off-Road Navigationcited by 0
  3. 2024BEVal: A Cross-dataset Evaluation Study of BEV Segmentation Models for Autonomous Drivingcited by 0
  4. 2025Exploring Single Domain Generalization of LiDAR-based Semantic Segmentation under Imperfect Labelscited by 0