  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 posed LiDAR measurements alone. We first leverage 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 clouds. We
then jointly optimize the feature embeddings and MLPs parameters with a
self-supervision paradigm for point cloud geometry and a pseudo-supervision
paradigm for semantic and panoptic labels. Subsequently, categories and
geometric structures for novel points are regressed, and marching cubes are
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. 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 LiDAR mapping methods.
