外观
论文arXiv 2609.3654329 天前
基于从部分观测学习耦合 TUDF 占据表示的面向规划 3D 场景补全
原文链接：Planning Oriented 3D Scene Completion via Coupled TUDF Occupancy Representation Learning from Partial Observations（arXiv 2609.36543 · 发布日期：2026-09-29）
Planning Oriented 3D Scene Completion via Coupled TUDF Occupancy Representation Learning from Partial Observations
针对部分可观测下导航规划受限，提出联合预测 TUDF 与占据的 3D 场景补全，并在未见环境验证重建和规划提升。
核心要点
- 部分传感覆盖与遮挡导致环境大范围未知，影响机器人导航中的路径规划。
- 方法以部分 LiDAR 观测为输入，联合预测 TUDF 连续几何表示和体素占据图。
- 双向耦合学习让 TUDF 指导占据重建，占据特征约束并细化距离场估计。
- 在未见环境的大量实验中，几何重建质量和下游规划性能均持续提升。
验证范围与局限：验证范围为未见环境实验，摘要未说明仿真或真机设置。
英文摘要 (Abstract)
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.
作者：Tianyou Yu, Pengfei Zhao, Chao Xu
来源
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