# Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform

arXiv: https://arxiv.org/abs/2008.09162 · HTML: https://mlsanity.com/paper/2008.09162/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2020.

## Built on by (most cited first, 30 of 40)

- [Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2302.07817/) (2023)
- [OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2304.05316/) (2023)
- [PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation](https://mlsanity.com/paper/2306.10013/) (2023)
- [CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving](https://mlsanity.com/paper/2207.12691/) (2022)
- [PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2308.16896/) (2023)
- [Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving Applications](https://mlsanity.com/paper/2311.17663/) (2023)
- [FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation](https://mlsanity.com/paper/2312.04484/) (2023)
- [Better Call SAL: Towards Learning to Segment Anything in Lidar](https://mlsanity.com/paper/2403.13129/) (2024)
- [LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds](https://mlsanity.com/paper/2210.08064/) (2022)
- [OccFusion: Multi-Sensor Fusion Framework for 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2403.01644/) (2024)
- [LODE: Locally Conditioned Eikonal Implicit Scene Completion from Sparse LiDAR](https://mlsanity.com/paper/2302.14052/) (2023)
- [Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation](https://mlsanity.com/paper/2207.02201/) (2022)
- [Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregation](https://mlsanity.com/paper/2202.13377/) (2022)
- [MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding](https://mlsanity.com/paper/2107.00346/) (2021)
- [PillarSegNet: Pillar-based Semantic Grid Map Estimation using Sparse LiDAR Data](https://mlsanity.com/paper/2105.04169/) (2021)
- [LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation](https://mlsanity.com/paper/2111.04875/) (2021)
- [InverseMatrixVT3D: An Efficient Projection Matrix-Based Approach for 3D Occupancy Prediction](https://mlsanity.com/paper/2401.12422/) (2024)
- [TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation](https://mlsanity.com/paper/2309.07849/) (2023)
- [Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments](https://mlsanity.com/paper/2208.07364/) (2022)
- [Lidar Panoptic Segmentation and Tracking without Bells and Whistles](https://mlsanity.com/paper/2310.12464/) (2023)
- [Deep Learning on 3D Semantic Segmentation: A Detailed Review](https://mlsanity.com/paper/2411.02104/) (2024)
- [Zero-Shot 4D Lidar Panoptic Segmentation](https://mlsanity.com/paper/2504.00848/) (2025)
- [SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point Cloud](https://mlsanity.com/paper/2406.16279/) (2024)
- [Filling Missing Values Matters for Range Image-Based Point Cloud Segmentation](https://mlsanity.com/paper/2405.10175/) (2024)
- [Future-Aware Interaction Network For Motion Forecasting](https://mlsanity.com/paper/2503.06565/) (2025)
- [VLM-Loc: Localization in Point Cloud Maps via Vision-Language Models](https://mlsanity.com/paper/2603.09826/) (2026)
- [PCB-RandNet: Rethinking Random Sampling for LIDAR Semantic Segmentation in Autonomous Driving Scene](https://mlsanity.com/paper/2209.13797/) (2022)
- [ElC-OIS: Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR Data](https://mlsanity.com/paper/2303.04351/) (2023)
- [Global-Aware Monocular Semantic Scene Completion with State Space Models](https://mlsanity.com/paper/2503.06569/) (2025)
- [Trainable Pointwise Decoder Module for Point Cloud Segmentation](https://mlsanity.com/paper/2408.01548/) (2024)

## Abstract (arXiv)

Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles, which are usually equipped with an embedded platform and have limited computational resources. Approaches that operate directly on the point cloud use complex spatial aggregation operations, which are very expensive and difficult to optimize for embedded platforms. They are therefore not suitable for real-time applications with embedded systems. As an alternative, projection-based methods are more efficient and can run on embedded platforms. However, the current state-of-the-art projection-based methods do not achieve the same accuracy as point-based methods and use millions of parameters. In this paper, we therefore propose a projection-based method, called Multi-scale Interaction Network (MINet), which is very efficient and accurate. The network uses multiple paths with different scales and balances the computational resources between the scales. Additional dense interactions between the scales avoid redundant computations and make the network highly efficient. The proposed network outperforms point-based, image-based, and projection-based methods in terms of accuracy, number of parameters, and runtime. Moreover, the network processes more than 24 scans per second on an embedded platform, which is higher than the framerates of LiDAR sensors. The network is therefore suitable for autonomous vehicles.