Domain-adaptive point cloud semantic segmentation (PCSS) is crucial for high-level autonomous driving. However, supervised deep learning methods are often constrained by training data and suffer from poor generalization in unknown environments. To address these challenges, we propose a domain-adaptive PCSS approach leveraging knowledge-augmented deep learning (KADL). Specifically, we introduce three strategies: (1) point cloud data augmentation based on the compact bird's-eye view (CBEV) map, which is a novel point cloud organization method; (2) implicit knowledge augmentation based on knowledge distillation; (3) explicit knowledge augmentation based on attribution analysis and network modulation. For experimental validation, we utilize two distinct datasets, namely the urban dataset SemanticKITTI and the off-road dataset RELLIS-3D, which are involved in the training and testing phases, respectively. Additionally, we have added road labels to the RELLIS-3D dataset, which originally lacked a road category. To our knowledge, this work is the first to investigate domain-adaptive PCSS from urban to off-road scenes. The experimental results demonstrate that our method is effective and has promising performance. The code and data are available at https://github.com/xfy0032/kadlpcss
Domain-Adaptive Point Cloud Semantic Segmentation from Urban to Off-Road Scenes Based on Knowledge-Augmented Deep Learning
2024-09-24
6372288 byte
Conference paper
Electronic Resource
English
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