# Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Study

arXiv: https://arxiv.org/abs/2004.11803 · HTML: https://mlsanity.com/paper/2004.11803/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

## Built on by (most cited first, 30 of 33)

- [RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation](https://mlsanity.com/paper/2103.12978/) (2021)
- [Rethinking Range View Representation for LiDAR Segmentation](https://mlsanity.com/paper/2303.05367/) (2023)
- [Segment Any Point Cloud Sequences by Distilling Vision Foundation Models](https://mlsanity.com/paper/2306.09347/) (2023)
- [EfficientLPS: Efficient LiDAR Panoptic Segmentation](https://mlsanity.com/paper/2102.08009/) (2021)
- [RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving](https://mlsanity.com/paper/2301.10222/) (2023)
- [LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2309.09256/) (2023)
- [GFNet: Geometric Flow Network for 3D Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2207.02605/) (2022)
- [Is Your LiDAR Placement Optimized for 3D Scene Understanding?](https://mlsanity.com/paper/2403.17009/) (2024)
- [Multi-Space Alignments Towards Universal LiDAR Segmentation](https://mlsanity.com/paper/2405.01538/) (2024)
- [MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding](https://mlsanity.com/paper/2107.00346/) (2021)
- [Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data](https://mlsanity.com/paper/2210.11750/) (2022)
- [PillarSegNet: Pillar-based Semantic Grid Map Estimation using Sparse LiDAR Data](https://mlsanity.com/paper/2105.04169/) (2021)
- [MaskRange: A Mask-classification Model for Range-view based LiDAR Segmentation](https://mlsanity.com/paper/2206.12073/) (2022)
- [An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models](https://mlsanity.com/paper/2405.14870/) (2024)
- [LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving](https://mlsanity.com/paper/2501.04005/) (2025)
- [Analyzing Deep Learning Representations of Point Clouds for Real-Time In-Vehicle LiDAR Perception](https://mlsanity.com/paper/2210.14612/) (2022)
- [What Can be Seen is What You Get: Structure Aware Point Cloud Augmentation](https://mlsanity.com/paper/2206.09664/) (2022)
- [Uncertainty-aware LiDAR Panoptic Segmentation](https://mlsanity.com/paper/2210.04472/) (2022)
- [Deep Learning on 3D Semantic Segmentation: A Detailed Review](https://mlsanity.com/paper/2411.02104/) (2024)
- [On the Calibration of Uncertainty Estimation in LiDAR-based Semantic Segmentation](https://mlsanity.com/paper/2308.02248/) (2023)
- [HALS: A Height-Aware Lidar Super-Resolution Framework for Autonomous Driving](https://mlsanity.com/paper/2202.03901/) (2022)
- [Filling Missing Values Matters for Range Image-Based Point Cloud Segmentation](https://mlsanity.com/paper/2405.10175/) (2024)
- [Context-aware Padding for Semantic Segmentation](https://mlsanity.com/paper/2109.07854/) (2021)
- [False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation](https://mlsanity.com/paper/2110.15681/) (2021)
- [Trainable Pointwise Decoder Module for Point Cloud Segmentation](https://mlsanity.com/paper/2408.01548/) (2024)
- [R3DPA: Leveraging 3D Representation Alignment and RGB Pretrained Priors for LiDAR Scene Generation](https://mlsanity.com/paper/2601.07692/) (2026)
- [Semi-Local Convolutions for LiDAR Scan Processing](https://mlsanity.com/paper/2111.15615/) (2021)
- [On the calibration of underrepresented classes in LiDAR-based semantic segmentation](https://mlsanity.com/paper/2210.06811/) (2022)
- [Improving Translation Invariance in Convolutional Neural Networks with Peripheral Prediction Padding](https://mlsanity.com/paper/2307.07725/) (2023)
- [From One to the Power of Many: Invariance to Multi-LiDAR Perception from Single-Sensor Datasets](https://mlsanity.com/paper/2409.18592/) (2024)

## Abstract (arXiv)

Autonomous vehicles need to have a semantic understanding of the three-dimensional world around them in order to reason about their environment. State of the art methods use deep neural networks to predict semantic classes for each point in a LiDAR scan. A powerful and efficient way to process LiDAR measurements is to use two-dimensional, image-like projections. In this work, we perform a comprehensive experimental study of image-based semantic segmentation architectures for LiDAR point clouds. We demonstrate various techniques to boost the performance and to improve runtime as well as memory constraints. First, we examine the effect of network size and suggest that much faster inference times can be achieved at a very low cost to accuracy. Next, we introduce an improved point cloud projection technique that does not suffer from systematic occlusions. We use a cyclic padding mechanism that provides context at the horizontal field-of-view boundaries. In a third part, we perform experiments with a soft Dice loss function that directly optimizes for the intersection-over-union metric. Finally, we propose a new kind of convolution layer with a reduced amount of weight-sharing along one of the two spatial dimensions, addressing the large difference in appearance along the vertical axis of a LiDAR scan. We propose a final set of the above methods with which the model achieves an increase of 3.2% in mIoU segmentation performance over the baseline while requiring only 42% of the original inference time.