Contextual Range-View Projection for 3D LiDAR Point Clouds

2026-01-26

AI Summary

This work addresses the semantic and structural information loss inherent in conventional range-view projection, which retains only the nearest point per pixel. To mitigate this limitation, the authors propose two novel projection mechanisms: Centerness-Aware Projection (CAP), which modulates depth values based on instance-centric distances to enhance preservation of object centers, and Class-Weighted-Aware Projection (CWAP), which incorporates class-specific weights to prioritize semantically important categories. Both methods operate within the standard range-view framework while significantly improving semantic completeness after projection. Evaluated on SemanticKITTI, CAP achieves up to a 3.1% mIoU improvement over the baseline, while CWAP effectively boosts performance for target classes with negligible impact on others.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning
• 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

Range-view projection provides an efficient method for transforming 3D LiDAR point clouds into 2D range image representations, enabling effective processing with 2D deep learning models. However, a major challenge in this projection is the many-to-one conflict, where multiple 3D points are mapped onto the same pixel in the range image, requiring a selection strategy. Existing approaches typically retain the point with the smallest depth (closest to the LiDAR), disregarding semantic relevance and object structure, which leads to the loss of important contextual information. In this paper, we extend the depth-based selection rule by incorporating contextual information from both instance centers and class labels, introducing two mechanisms: \textit{Centerness-Aware Projection (CAP)} and \textit{Class-Weighted-Aware Projection (CWAP)}. In CAP, point depths are adjusted according to their distance from the instance center, thereby prioritizing central instance points over noisy boundary and background points. In CWAP, object classes are prioritized through user-defined weights, offering flexibility in the projection strategy. Our evaluations on the SemanticKITTI dataset show that CAP preserves more instance points during projection, achieving up to a 3.1\% mIoU improvement compared to the baseline. Furthermore, CWAP enhances the performance of targeted classes while having a negligible impact on the performance of other classes

Problem

• Range-view projection
• 3D LiDAR point clouds
• many-to-one conflict
• contextual information loss
• instance structure

Innovation

• Range-view projection
• Centerness-Aware Projection
• Class-Weighted-Aware Projection
• 3D LiDAR point clouds
• Contextual information