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

arXiv: https://arxiv.org/abs/2105.04169 · HTML: https://mlsanity.com/paper/2105.04169/

## Key facts

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

## Builds on (most cited first, 14 of 14)

- [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)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [What You See is What You Get: Exploiting Visibility for 3D Object Detection](https://mlsanity.com/paper/1912.04986/) (2019)
- [Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform](https://mlsanity.com/paper/2008.09162/) (2020)
- [Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Study](https://mlsanity.com/paper/2004.11803/) (2020)
- [SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation](https://mlsanity.com/paper/2009.12276/) (2020)
- [Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks](https://mlsanity.com/paper/1805.08689/) (2018)
- [Motion Estimation in Occupancy Grid Maps in Stationary Settings Using Recurrent Neural Networks](https://mlsanity.com/paper/1909.11387/) (2019)
- [Exploiting Multi-Layer Grid Maps for Surround-View Semantic Segmentation of Sparse LiDAR Data](https://mlsanity.com/paper/2005.06667/) (2020)

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

- [PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye View](https://mlsanity.com/paper/2306.10761/) (2023)
- [MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding](https://mlsanity.com/paper/2107.00346/) (2021)
- [Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles](https://mlsanity.com/paper/2302.08292/) (2023)
- [AGO: Adaptive Grounding for Open World 3D Occupancy Prediction](https://mlsanity.com/paper/2504.10117/) (2025)
- [Safe Navigation in Unstructured Environments by Minimizing Uncertainty in Control and Perception](https://mlsanity.com/paper/2306.14601/) (2023)
- [UFO: Uncertainty-aware LiDAR-image Fusion for Off-road Semantic Terrain Map Estimation](https://mlsanity.com/paper/2403.02642/) (2024)
- [Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian Inference](https://mlsanity.com/paper/2108.03180/) (2021)
- [HiLoTs: High-Low Temporal Sensitive Representation Learning for Semi-Supervised LiDAR Segmentation in Autonomous Driving](https://mlsanity.com/paper/2503.17752/) (2025)
- [LAPTNet-FPN: Multi-scale LiDAR-aided Projective Transform Network for Real Time Semantic Grid Prediction](https://mlsanity.com/paper/2302.06414/) (2023)
- [Circular Accessible Depth: A Robust Traversability Representation for UGV Navigation](https://mlsanity.com/paper/2212.13676/) (2022)
- [Semantic Segmentation on 3D Point Clouds with High Density Variations](https://mlsanity.com/paper/2307.01489/) (2023)
- [Hierarchical Insights: Exploiting Structural Similarities for Reliable 3D Semantic Segmentation](https://mlsanity.com/paper/2404.06124/) (2024)
- [LAPTNet: LiDAR-Aided Perspective Transform Network](https://mlsanity.com/paper/2211.14445/) (2022)
- [METAVerse: Meta-Learning Traversability Cost Map for Off-Road Navigation](https://mlsanity.com/paper/2307.13991/) (2023)
- [BEVal: A Cross-dataset Evaluation Study of BEV Segmentation Models for Autonomous Driving](https://mlsanity.com/paper/2408.16322/) (2024)
- [Exploring Single Domain Generalization of LiDAR-based Semantic Segmentation under Imperfect Labels](https://mlsanity.com/paper/2510.09035/) (2025)

## 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.