# Simulation-based Lidar Super-resolution for Ground Vehicles

arXiv: https://arxiv.org/abs/2004.05242 · HTML: https://mlsanity.com/paper/2004.05242/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2020.

## Built on by (most cited first, 19 of 19)

- [LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2309.09256/) (2023)
- [A Survey on Deep Domain Adaptation for LiDAR Perception](https://mlsanity.com/paper/2106.02377/) (2021)
- [Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey](https://mlsanity.com/paper/2308.12113/) (2023)
- [TULIP: Transformer for Upsampling of LiDAR Point Clouds](https://mlsanity.com/paper/2312.06733/) (2023)
- [Lidar Upsampling with Sliced Wasserstein Distance](https://mlsanity.com/paper/2301.13558/) (2023)
- [Advancing Image Super-resolution Techniques in Remote Sensing: A Comprehensive Survey](https://mlsanity.com/paper/2505.23248/) (2025)
- [SRMamba: Mamba for Super-Resolution of LiDAR Point Clouds](https://mlsanity.com/paper/2505.10601/) (2025)
- [Dense Representative Tooth Landmark/axis Detection Network on 3D Model](https://mlsanity.com/paper/2111.04212/) (2021)
- [HALS: A Height-Aware Lidar Super-Resolution Framework for Autonomous Driving](https://mlsanity.com/paper/2202.03901/) (2022)
- [Fast LiDAR Upsampling using Conditional Diffusion Models](https://mlsanity.com/paper/2405.04889/) (2024)
- [Empir3D : A Framework for Multi-Dimensional Point Cloud Assessment](https://mlsanity.com/paper/2306.03660/) (2023)
- [PU-Ray: Domain-Independent Point Cloud Upsampling via Ray Marching on Neural Implicit Surface](https://mlsanity.com/paper/2310.08755/) (2023)
- [SRMambaV2: Biomimetic Attention for Sparse Point Cloud Upsampling in Autonomous Driving](https://mlsanity.com/paper/2507.17479/) (2025)
- [Guided Model-based LiDAR Super-Resolution for Resource-Efficient Automotive scene Segmentation](https://mlsanity.com/paper/2509.01317/) (2025)
- [Real-Time LiDAR Super-Resolution via Frequency-Aware Multi-Scale Fusion](https://mlsanity.com/paper/2511.07377/) (2025)
- [A holistic perception system of internal and external monitoring for ground autonomous vehicles: AutoTRUST paradigm](https://mlsanity.com/paper/2508.17969/) (2025)
- [ALICE-LRI: A General Method for Lossless Range Image Generation for Spinning LiDAR Sensors without Calibration Metadata](https://mlsanity.com/paper/2510.20708/) (2025)
- [A Comprehensive Survey on Deep Learning-Based LiDAR Super-Resolution for Autonomous Driving](https://mlsanity.com/paper/2602.15904/) (2026)
- [Fast and Accurate Outlier-Aware LiDAR Super-Resolution for SLAM Applications](https://mlsanity.com/paper/2606.28607/) (2026)

## Abstract (arXiv)

We propose a methodology for lidar super-resolution with ground vehicles driving on roadways, which relies completely on a driving simulator to enhance, via deep learning, the apparent resolution of a physical lidar. To increase the resolution of the point cloud captured by a sparse 3D lidar, we convert this problem from 3D Euclidean space into an image super-resolution problem in 2D image space, which is solved using a deep convolutional neural network. By projecting a point cloud onto a range image, we are able to efficiently enhance the resolution of such an image using a deep neural network. Typically, the training of a deep neural network requires vast real-world data. Our approach does not require any real-world data, as we train the network purely using computer-generated data. Thus our method is applicable to the enhancement of any type of 3D lidar theoretically. By novelly applying Monte-Carlo dropout in the network and removing the predictions with high uncertainty, our method produces high accuracy point clouds comparable with the observations of a real high resolution lidar. We present experimental results applying our method to several simulated and real-world datasets. We argue for the method's potential benefits in real-world robotics applications such as occupancy mapping and terrain modeling.