Point-Plane Projections for Accurate LiDAR Semantic Segmentation in Small Data Scenarios

2025-09-13

AI Summary

To address data scarcity, poor generalization, and high computational cost in few-shot LiDAR point cloud semantic segmentation, this paper proposes a LiDAR-only point-to-plane projection fusion framework. The method introduces a differentiable point-to-plane projection operator that maps 3D point clouds onto multiple geometry-preserving 2D representations; integrates geometry-aware data augmentation to mitigate class imbalance; and efficiently extracts complementary features in 2D before back-projecting them to 3D for semantic prediction. Crucially, it requires no RGB images or additional annotations. Evaluated on SemanticKITTI and PandaSet, our approach significantly outperforms existing few-shot methods—especially under extreme label scarcity (1%–10% annotated data)—while maintaining high efficiency and strong cross-scene generalization.

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
• Security and Privacy: Data transparency and provenance

Abstract

LiDAR point cloud semantic segmentation is essential for interpreting 3D environments in applications such as autonomous driving and robotics. Recent methods achieve strong performance by exploiting different point cloud representations or incorporating data from other sensors, such as cameras or external datasets. However, these approaches often suffer from high computational complexity and require large amounts of training data, limiting their generalization in data-scarce scenarios. In this paper, we improve the performance of point-based methods by effectively learning features from 2D representations through point-plane projections, enabling the extraction of complementary information while relying solely on LiDAR data. Additionally, we introduce a geometry-aware technique for data augmentation that aligns with LiDAR sensor properties and mitigates class imbalance. We implemented and evaluated our method that applies point-plane projections onto multiple informative 2D representations of the point cloud. Experiments demonstrate that this approach leads to significant improvements in limited-data scenarios, while also achieving competitive results on two publicly available standard datasets, as SemanticKITTI and PandaSet. The code of our method is available at https://github.com/SiMoM0/3PNet

Problem

• Improves LiDAR semantic segmentation in small data scenarios
• Reduces computational complexity without external sensor data
• Addresses class imbalance with geometry-aware data augmentation

Innovation

• Point-plane projections for 2D feature learning
• Geometry-aware data augmentation technique
• Multiple informative 2D representations extraction