AI 中文总结
针对海上船舶3D重建中现有NBV策略忽略方向观测历史的问题，本文提出DA-NBV策略，结合PAF等方法，在SeaShip-3D数据集与仿真环境下实现了更高效的船舶3D重建性能。
AI 中文摘要
海上船舶的精确3D重建对于海事监管、损伤评估和自主海事作业至关重要。尽管3D重建技术已取得显著进展，但高质量数据采集仍在很大程度上依赖人工设计的轨迹或熟练操作员，导致成本高且可扩展性有限。最优下一个视点（NBV）规划通过基于当前状态选择后续视点来自动化这一过程，但现有NBV策略主要对空间占用进行建模，却忽略了方向观测历史，这一局限对船舶而言尤为突出：其复杂的上层建筑和严重的自遮挡需要从多个视点进行观测，而方向覆盖不足往往会导致重建不完整。海上环境中，波浪引起的升沉、横摇和纵倾会持续改变船舶的姿态和表面可见性，进一步加剧了这些挑战；同时，风扰和有限的舰载功率对扫描效率提出了更严格的要求。为应对这些挑战，本文提出DA-NBV，一种方向感知NBV策略，它通过方向观测统计数据增强传统占用状态；引入可学习的位置优势场（PAF），利用方向信息指导视点选择；该策略还采用局部约束动作空间和非线性覆盖塑形奖励来提高扫描效率。此外，本文开发了面向船舶的SeaShip-3D数据集和可配置海况仿真环境。在不同升沉、横摇和纵倾条件下的实验表明，DA-NBV将重建完整性提高了约3个百分点，使倒角距离降低了43%，同时实现了更高的路径效率。
英文摘要
Accurate 3D reconstruction of ships at sea is important for maritime supervision, damage assessment, and autonomous maritime operations. Although 3D reconstruction has advanced considerably, high-quality data acquisition still largely relies on manually designed trajectories or skilled operators, resulting in high costs and limited scalability. Next-best-view (NBV) planning automates this process by selecting subsequent viewpoints based on the current state. However, existing NBV policies mainly model spatial occupancy while overlooking directional observation history. This limitation is particularly problematic for ships: their complex superstructures and severe self-occlusions require observations from multiple viewpoints, and insufficient directional coverage often yields incomplete reconstructions. These challenges are further amplified at sea, where wave-induced heave, roll, and pitch continuously alter the ship's pose and surface visibility. Meanwhile, wind disturbances and limited onboard power impose stricter requirements on scanning efficiency. To address these challenges, we propose DA-NBV, a direction-aware NBV policy that augments the conventional occupancy state with directional observation statistics. We introduce a learnable Position Advantage Field (PAF) that uses directional information to guide viewpoint selection. The policy further adopts a locally constrained action space and a nonlinear coverage-shaping reward to improve scanning efficiency. We also develop the ship-oriented SeaShip-3D dataset and a configurable sea-state simulation environment. Experiments under varying heave, roll, and pitch conditions show that DA-NBV improves reconstruction completeness by approximately 3 percentage points and reduces Chamfer distance by 43% while achieving higher path efficiency.
Comments16pages, 11 figures
