基于三层采样与全景表示的城市级增量神经建图
City-scale Incremental Neural Mapping with Three-layer Sampling and Panoptic Representation
AI总结：
本文针对稀疏LiDAR输入下的城市级持续神经建图问题，提出三层采样策略与融合环境级和实例级建模的全景表示，并在SemanticKITTI上验证了其有效性。
摘要：
神经隐式表示近来在机器人领域受到广泛关注，因为它们具有表达性强、连续且紧凑的特点。然而，基于稀疏LiDAR输入的城市级持续隐式稠密建图仍然是一个尚未充分探索的挑战。为此，我们成功构建了一个城市级持续神经建图系统，该系统采用由环境级建模和实例级建模组成的全景表示。给定稀疏LiDAR点云流，系统维护一个动态生成模型，将三维坐标映射为有符号距离场（SDF）值。为解决城市级空间中不同层级几何信息表示的困难，我们提出了一种定制的三层采样策略，动态采样全局域、局部域和近表面域。同时，为实现在不完整观测下对实例的高保真建图，引入类别特定先验以更好地建模几何细节。我们在公开的SemanticKITTI数据集上进行了评估，并通过定量和定性结果证明了新提出的三层采样策略和全景表示的重要性。代码和模型将公开发布。
英文摘要：
Neural implicit representations are drawing a lot of attention from the robotics community recently, as they are expressive, continuous and compact. However, city-scale continual implicit dense mapping based on sparse LiDAR input is still an under-explored challenge. To this end, we successfully build a city-scale continual neural mapping system with a panoptic representation that consists of environment-level and instance-level modelling. Given a stream of sparse LiDAR point cloud, it maintains a dynamic generative model that maps 3D coordinates to signed distance field (SDF) values. To address the difficulty of representing geometric information at different levels in city-scale space, we propose a tailored three-layer sampling strategy to dynamically sample the global, local and near-surface domains. Meanwhile, to realize high fidelity mapping of instance under incomplete observation, category-specific prior is introduced to better model the geometric details. We evaluate on the public SemanticKITTI dataset and demonstrate the significance of the newly proposed three-layer sampling strategy and panoptic representation, using both quantitative and qualitative results. Codes and model will be publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
