INF-SLiM: Large-scale Implicit Neural Fields for Semantic LiDAR Mapping of Embodied AI Agents

Large-scale semantic mapping is crucial for outdoor autonomous agents to perform high-level tasks such as planning and navigation. In this paper, we propose a novel method for large-scale 3D semantic reconstruction through implicit representations from posed LiDAR measurements alone. We first leverage an octree-based hierarchical structure to store implicit features, then these implicit features are decoded to signed distance value and semantic information through shallow Multilayer Perceptrons (MLPs). We leverage radial window self-attention networks to predict the semantic labels of point clouds. We then jointly optimize the feature embedding and MLP parameters with a self-supervision paradigm for point-cloud geometry and a pseudo-supervision paradigm for semantic and panoptic labels. Subsequently, geometric structures and object categories for novel points in the unseen area are regressed, and the marching cubes method is exploited to subdivide and visualize scenes in the inferring stage. Experiments on two real-world datasets, SemanticKITTI and SemanticPOSS, demonstrate the superior segmentation efficiency and mapping effectiveness of our framework compared to current state-of-the-art 3D semantic LiDAR mapping methods. Supplementary Material File (_jfr_inf_slim__semantic_lidar_mapping_via_implicit_neural_fields_for_large_scale_3d_scenesv5.pdf) - Download - 5.35 MB Information & Authors Information Version history Peer review timeline Published Journal of Field Robotics Version of Record1 Sep 2025Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection

Authors Metrics & Citations Metrics Article Usage 528views 308downloads Citations Download citation Jianyuan Zhang, Zhiliu Yang, Meng Zhang, et al. INF-SLiM: Large-scale Implicit Neural Fields for Semantic LiDAR Mapping of Embodied AI Agents. Authorea. 02 January 2025. DOI: https://doi.org/10.22541/au.173582686.69413624/v1 DOI: https://doi.org/10.22541/au.173582686.69413624/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.