ReviewSensors (Basel, Switzerland)2022
A Survey on Deep-Learning-Based LiDAR 3D Object Detection for Autonomous Driving.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed.
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- Development of a Micromobility Riding Evaluation Platform for an Indoor Riding Lane Based on Multi-View Overhead Video Integration.Sensors (Basel, Switzerland) · 2026Article
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- A Three-Dimensional LiDAR Observability Framework for Pedestrian Representation: Sensor Placement and Multi-View Fusion on a Compact Autonomous Vehicle.Sensors (Basel, Switzerland) · 2026Article
- Bounded Graph Conditioning for LiDAR 3D Object Detection Under Sensor Degradation.Sensors (Basel, Switzerland) · 2026Article
- The method of reflection-based marker detection and identification to ensure accurate AGV docking.Scientific reports · 2025Article
- Optical-Structural Optimization for Condensation Suppression in Automotive Camera Modules.Sensors (Basel, Switzerland) · 2025Article
- IFE-CMT: Instance-Aware Fine-Grained Feature Enhancement Cross Modal Transformer for 3D Object Detection.Sensors (Basel, Switzerland) · 2025Article
- Geometrically aware transformer for point cloud analysis.Scientific reports · 2025Article
- LiDAR Technology for UAV Detection: From Fundamentals and Operational Principles to Advanced Detection and Classification Techniques.Sensors (Basel, Switzerland) · 2025Review
- PillarFocusNet for 3D object detection with perceptual diffusion and key feature understanding.Scientific reports · 2025Article
- S2*-ODM: Dual-Stage Improved PointPillar Feature-Based 3D Object Detection Method for Autonomous Driving.Sensors (Basel, Switzerland) · 2025Article
- Point-Cloud Instance Segmentation for Spinning Laser Sensors.Journal of imaging · 2024Article
- Research on the Laser Scattering Characteristics of Three-Dimensional Imaging Based on Electro-Optical Crystal Modulation.Micromachines · 2024Article
- Research on Polarization Modulation of Electro-Optical Crystals for 3D Imaging Reconstruction.Micromachines · 2024Article
- Classification of Lung Diseases Using an Attention-Based Modified DenseNet Model.Journal of imaging informatics in medicine · 2024Article
- Improved DeepSORT-Based Object Tracking in Foggy Weather for AVs Using Sematic Labels and Fused Appearance Feature Network.Sensors (Basel, Switzerland) · 2024Article
- Edge-Triggered Three-Dimensional Object Detection Using a LiDAR Ring.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
LiDAR is a commonly used sensor for autonomous driving to make accurate, robust, and fast decision-making when driving. The sensor is used in the perception system, especially object detection, to understand the driving environment. Although 2D object detection has succeeded during the deep-learning era, the lack of depth information limits understanding of the driving environment and object location. Three-dimensional sensors, such as LiDAR, give 3D information about the surrounding environment, which is essential for a 3D perception system. Despite the attention of the computer vision community to 3D object detection due to multiple applications in robotics and autonomous driving, there are challenges, such as scale change, sparsity, uneven distribution of LiDAR data, and occlusions. Different representations of LiDAR data and methods to minimize the effect of the sparsity of LiDAR data have been proposed. This survey presents the LiDAR-based 3D object detection and feature-extraction techniques for LiDAR data. The 3D coordinate systems differ in camera and LiDAR-based datasets and methods. Therefore, the commonly used 3D coordinate systems are summarized. Then, state-of-the-art LiDAR-based 3D object-detection methods are reviewed with a selected comparison among methods.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.