A Comprehensive Survey on Deep Learning-Based LiDAR Super-Resolution for Autonomous Driving

2026-02-15

AI Summary

This work addresses the challenge that sparse point clouds generated by low-cost, low-resolution LiDARs struggle to meet the high-precision environmental perception demands of autonomous driving. It presents the first systematic survey of deep learning–based LiDAR super-resolution techniques tailored for autonomous vehicles, introducing a unified taxonomy that categorizes existing methods into four classes: CNN-based approaches, model-driven deep unfolding, implicit neural representations, and Transformer- and Mamba-based architectures. The study comprehensively reviews foundational components—including data representation, problem formulation, benchmark datasets, and evaluation metrics—and identifies emerging trends such as range-image representation, extreme model compression, and resolution-flexible architectures. Furthermore, it highlights critical challenges in real-world deployment and outlines promising future directions, offering both theoretical insights and practical pathways toward real-time inference, cross-sensor generalization, and engineering implementation.

Technology Category

• Computer Vision: Representation Learning for Vision
• Machine Learning: Deep Neural Architectures and Foundation Models
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

LiDAR sensors are often considered essential for autonomous driving, but high-resolution sensors remain expensive while affordable low-resolution sensors produce sparse point clouds that miss critical details. LiDAR super-resolution addresses this challenge by using deep learning to enhance sparse point clouds, bridging the gap between different sensor types and enabling cross-sensor compatibility in real-world deployments. This paper presents the first comprehensive survey of LiDAR super-resolution methods for autonomous driving. Despite the importance of practical deployment, no systematic review has been conducted until now. We organize existing approaches into four categories: CNN-based architectures, model-based deep unrolling, implicit representation methods, and Transformer and Mamba-based approaches. We establish fundamental concepts including data representations, problem formulation, benchmark datasets and evaluation metrics. Current trends include the adoption of range image representation for efficient processing, extreme model compression and the development of resolution-flexible architectures. Recent research prioritizes real-time inference and cross-sensor generalization for practical deployment. We conclude by identifying open challenges and future research directions for advancing LiDAR super-resolution technology.

Problem

• LiDAR super-resolution
• autonomous driving
• sparse point clouds
• deep learning
• cross-sensor compatibility

Innovation

• LiDAR super-resolution
• deep learning
• autonomous driving
• point cloud enhancement
• cross-sensor generalization