Learning Geometric and Photometric Features from Panoramic LiDAR Scans for Outdoor Place Categorization

2026-03-13

AI Summary

This work addresses the challenge of semantic place classification in outdoor environments, where performance is often degraded by varying illumination conditions and dynamic occlusions. To tackle this problem, the authors propose a multimodal LiDAR-based approach that leverages both omnidirectional depth and reflectance images. They introduce the first large-scale, multi-sensor-annotated panoramic 3D outdoor (MPO) dataset and employ a convolutional neural network to jointly learn geometric and photometric features from the two modalities. The method achieves high-accuracy classification across six outdoor scene categories. Experimental results demonstrate significant improvements over existing state-of-the-art techniques, confirming the effectiveness of dual-modality fusion in enhancing LiDAR-based semantic understanding and offering a promising direction for autonomous navigation of self-driving vehicles in previously unseen environments.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

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: Multilingual and cross-lingual Web search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Semantic place categorization, which is one of the essential tasks for autonomous robots and vehicles, allows them to have capabilities of self-decision and navigation in unfamiliar environments. In particular, outdoor places are more difficult targets than indoor ones due to perceptual variations, such as dynamic illuminance over twenty-four hours and occlusions by cars and pedestrians. This paper presents a novel method of categorizing outdoor places using convolutional neural networks (CNNs), which take omnidirectional depth/reflectance images obtained by 3D LiDARs as the inputs. First, we construct a large-scale outdoor place dataset named Multi-modal Panoramic 3D Outdoor (MPO) comprising two types of point clouds captured by two different LiDARs. They are labeled with six outdoor place categories: coast, forest, indoor/outdoor parking, residential area, and urban area. Second, we provide CNNs for LiDAR-based outdoor place categorization and evaluate our approach with the MPO dataset. Our results on the MPO dataset outperform traditional approaches and show the effectiveness in which we use both depth and reflectance modalities. To analyze our trained deep networks we visualize the learned features.

Problem

• outdoor place categorization
• semantic place categorization
• perceptual variations
• LiDAR scans
• autonomous navigation

Innovation

• panoramic LiDAR
• outdoor place categorization
• depth-reflectance fusion
• convolutional neural networks
• multimodal 3D dataset