Sea-SHINE: Semantic-Aware 3D Neural Mapping Using Implicit Representations

Abstract

Semantic-aware mapping is crucial for advancing robotic navigation and interaction within complex environments. Traditional 3D mapping techniques primarily capture geometric details, missing the semantic richness necessary for autonomous systems to understand their surroundings comprehensively. This paper presents Sea-SHINE, a novel approach that integrates semantic information within a neural implicit mapping framework for large-scale environments. Our method enhances the utility and navigational relevance of 3D maps by embedding semantic awareness into the mapping process, allowing robots to recognize, understand, and reconstruct environments effectively. The proposed system leverages dual decoders and a semantic awareness module, which utilizes Feature-wise Linear Modulation (FiLM) to condition mapping on semantic labels. Extensive experiments on datasets such as SemanticKITTI, KITTI-360, and ITLP-Campus demonstrate significant improvements in map precision and recall, particularly in recognizing crucial objects like road signs. Our implementation bridges the gap between geometric accuracy and semantic understanding, fostering a deeper interaction between robots and their operational environments. The code is publicly available at https://github.com/VitalyyBezuglyj/Sea-SHINE.