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

2025-09-01

AI Summary

To address the degraded 3D semantic segmentation accuracy caused by the sparsity of low-cost 16-beam LiDAR point clouds, this paper proposes the first end-to-end joint optimization framework that unifies point cloud super-resolution (SR) and semantic segmentation. Methodologically, we design a lightweight SR network and introduce a semantics-guided region-weighted loss function to emphasize critical object regions, enabling collaborative optimization of point cloud completion and segmentation through joint training. Our key contribution lies in explicitly embedding semantic information into the SR reconstruction process—marking the first such effort—thereby circumventing error accumulation inherent in conventional two-stage pipelines. Experiments demonstrate that, using only 16-beam LiDAR data, our method achieves segmentation performance on par with a 64-beam LiDAR baseline (12.3% mIoU improvement), while reducing model parameters by 67%. This significantly enhances both accuracy and efficiency for autonomous driving systems under resource constraints.

Technology Category

• Computer Vision: Segmentation
• Search and Optimization: Learning to Search
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

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

Abstract

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.

Problem

• Improving 3D segmentation with low-cost sparse LiDAR data
• Jointly optimizing super-resolution and semantic segmentation tasks
• Reducing computational costs while maintaining high segmentation accuracy

Innovation

• Jointly optimizes LiDAR super-resolution and semantic segmentation
• Uses lightweight model-based architecture with fewer parameters
• Introduces new loss function focusing on regions of interest