Autonomous UAV path planning for 3D reconstruction has been actively studied in various applications for high-quality 3D models. However, most existing works have adopted explore-then-exploit, prior-based or exploration-based strategies, demonstrating inefficiency with repeated flight and low autonomy. In this paper, we propose PredRecon, a prediction-boosted planning framework that can autonomously generate paths for high 3D reconstruction quality. We obtain inspiration from humans can roughly infer the complete construction structure from partial observation. Hence, we devise a surface prediction module (SPM) to predict the coarse complete surfaces of the target from the current partial reconstruction. Then, the uncovered surfaces are produced by online volumetric mapping waiting for observation by UAV. Lastly, a hierarchical planner plans motions for 3D reconstruction, which sequentially finds efficient global coverage paths, plans local paths for maximizing the performance of Multi-View Stereo (MVS), and generates smooth trajectories for image-pose pairs acquisition. We conduct benchmarks in the realistic simulator, which validates the performance of PredRecon compared with the classical and state-of-the-art methods. The open-source code is released at https://github.com/HKUST-Aerial-Robotics/PredRecon.

翻译：自主无人机三维重建路径规划在多种应用中已被广泛研究，旨在获取高质量的三维模型。然而，现有工作大多采用先探索后利用、基于先验或基于探索的策略，存在重复飞行效率低下及自主性不足的问题。本文提出PredRecon——一种预测增强的规划框架，能够自主生成路径以实现高质量的三维重建。受人类可从局部观测大致推断完整建筑结构的启发，我们设计了一个表面预测模块（SPM），用于根据当前局部重建结果预测目标的粗略完整表面。随后，通过在线体素映射生成未被覆盖的表面，等待无人机观测。最后，采用分层规划器为三维重建规划运动：依次寻找高效的全局覆盖路径、规划最大化多视角立体（MVS）性能的局部路径，并生成用于获取图像-位姿对的平滑轨迹。我们在逼真仿真器中进行基准测试，验证了PredRecon相较于经典及最新方法的性能。开源代码已发布于https://github.com/HKUST-Aerial-Robotics/PredRecon。