LISNeRF Mapping: LiDAR-based Implicit Mapping via Semantic Neural Fields for Large-Scale 3D Scenes

Large-scale semantic mapping is crucial for outdoor autonomous agents to fulfill high-level tasks such as planning and navigation. This paper proposes a novel method for large-scale 3D semantic reconstruction through implicit representations from LiDAR measurements alone. We firstly leverages an octree-based and hierarchical structure to store implicit features, then these implicit features are decoded to semantic information and signed distance value through shallow Multilayer Perceptrons (MLPs). We adopt off-the-shelf algorithms to predict the semantic labels and instance IDs of point cloud. Then we jointly optimize the implicit features and MLPs parameters with self-supervision paradigm for point cloud geometry and pseudo-supervision pradigm for semantic and panoptic labels. Subsequently, Marching Cubes algorithm is exploited to subdivide and visualize the scenes in the inferring stage. For scenarios with memory constraints, a map stitching strategy is also developed to merge sub-maps into a complete map. As far as we know, our method is the first work to reconstruct semantic implicit scenes from LiDAR-only input. Experiments on three real-world datasets, SemanticKITTI, SemanticPOSS and nuScenes, demonstrate the effectiveness and efficiency of our framework compared to current state-of-the-art 3D mapping methods.