# Guided Model-based LiDAR Super-Resolution for Resource-Efficient Automotive scene Segmentation

arXiv: https://arxiv.org/abs/2509.01317 · HTML: https://mlsanity.com/paper/2509.01317/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 1 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://mlsanity.com/paper/1911.11236/) (2019)
- [(AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network](https://mlsanity.com/paper/2102.04530/) (2021)
- [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020)
- [Rethinking Range View Representation for LiDAR Segmentation](https://mlsanity.com/paper/2303.05367/) (2023)
- [SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances](https://mlsanity.com/paper/2002.09147/) (2020)
- [Lidar for Autonomous Driving: The principles, challenges, and trends for automotive lidar and perception systems](https://mlsanity.com/paper/2004.08467/) (2020)
- [FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decoding](https://mlsanity.com/paper/2109.03787/) (2021)
- [Simulation-based Lidar Super-resolution for Ground Vehicles](https://mlsanity.com/paper/2004.05242/) (2020)
- [Implicit LiDAR Network: LiDAR Super-Resolution via Interpolation Weight Prediction](https://mlsanity.com/paper/2203.06413/) (2022)
- [Lidar Upsampling with Sliced Wasserstein Distance](https://mlsanity.com/paper/2301.13558/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Comprehensive Survey on Deep Learning-Based LiDAR Super-Resolution for Autonomous Driving](https://mlsanity.com/paper/2602.15904/) (2026)

## Abstract (arXiv)

High-resolution LiDAR data plays a critical role in 3D semantic segmentation for autonomous driving, but the high cost of advanced sensors limits large-scale deployment. In contrast, low-cost sensors such as 16-channel LiDAR produce sparse point clouds that degrade segmentation accuracy. To overcome this, we introduce the first end-to-end framework that jointly addresses LiDAR super-resolution (SR) and semantic segmentation. The framework employs joint optimization during training, allowing the SR module to incorporate semantic cues and preserve fine details, particularly for smaller object classes. A new SR loss function further directs the network to focus on regions of interest. The proposed lightweight, model-based SR architecture uses significantly fewer parameters than existing LiDAR SR approaches, while remaining easily compatible with segmentation networks. Experiments show that our method achieves segmentation performance comparable to models operating on high-resolution and costly 64-channel LiDAR data.