面向规划的3D场景补全：基于部分观测的耦合TUDF占用表示学习
Planning Oriented 3D Scene Completion via Coupled TUDF Occupancy Representation Learning from Partial Observations
- Institute of Cyber-System and Control, College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院网络系统与控制研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出面向路径规划的3D场景补全框架，通过耦合TUDF连续几何表示与体素占用图，利用双向学习提升重建质量并直接支持轨迹规划，实验验证了其有效性。
AI中文摘要：
部分可观测性仍然是机器人导航中的一个基本挑战，有限的传感器覆盖范围和遮挡使得环境中大部分区域未被观测到。现有的场景补全方法主要侧重于改进不完整的地图构建或重建部分观测的3D结构，但很少研究如何设计场景补全以有益于路径规划等下游任务。在这项工作中，我们提出了一种面向路径规划的3D场景补全框架，该框架超越了纯占用建模，转向耦合的几何公式。具体而言，给定部分LiDAR观测作为输入，所提出的框架联合预测基于截断无符号距离场（TUDF）的连续几何表示和体素级占用图。这种耦合表示使网络能够更好地推理障碍物边界和自由空间几何。为了充分利用两种表示之间的协同作用，我们引入了一种双向耦合学习方案，其中TUDF特征提供密集的几何指导以改善占用重建，而占用特征反过来提供补充的结构约束以细化距离场估计。因此，所提出的网络直接预测完整的占用和TUDF表示，允许TUDF无缝集成到轨迹规划中而无需后处理。在未见环境上的大量实验表明，所提出的方法持续改善了几何重建质量和下游规划性能。
英文摘要：
Partial observability remains a fundamental challenge in robotic navigation, where limited sensor coverage and occlusions leave large portions of the environment unobserved. Existing scene completion methods primarily focus on improving incomplete mapping or reconstructing partially observed 3D structures, but rarely investigate how scene completion can be designed to benefit downstream tasks such as path planning. In this work, we propose a path-planning-oriented 3D scene completion framework that moves beyond pure occupancy modeling toward a coupled geometric formulation. Specifically, given partial LiDAR observations as input, the proposed framework jointly predicts completed Truncated Unsigned Distance Field (TUDF)-based continuous geometric representations and voxel-wise occupancy maps. This coupled representation allows the network to better reason about obstacle boundaries and free-space geometry. To fully exploit the synergy between the two representations, we introduce a bidirectionally coupled learning scheme, where TUDF features provide dense geometric guidance to improve occupancy reconstruction, while occupancy features in turn offer complementary structural constraints that refine distance-field estimation. Consequently, the proposed network directly predicts complete occupancy and TUDF representations, allowing seamless integration of TUDF into trajectory planning without post-processing. Extensive experiments on unseen environments demonstrate that the proposed method consistently improves both geometric reconstruction quality and downstream planning performance.
