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.

本文提出了一种通过 LiDAR 测量单独重建大规模三维语义场景的新方法，使用隐式表示进行建模。通过浅层多层感知机（MLPs）从隐式特征解码出语义信息和有符号距离值，并采用自监督策略和伪监督策略进行隐式特征和 MLPs 参数的联合优化，最后利用 Marching Cubes 算法对场景进行推断阶段的细分和可视化。与当前最先进的 3D 建图方法相比，我们的框架在三个真实数据集，SemanticKITTI，SemanticPOSS 和 nuScenes 上表现出了很高的效果和效率。