PredRecon：一种面向快速高质量自主空中重建的预测增强规划框架
PredRecon: A Prediction-boosted Planning Framework for Fast and High-quality Autonomous Aerial Reconstruction
AI总结：
PredRecon通过表面预测模块从部分重建推断完整表面，并结合分层规划器依次优化全局覆盖、局部MVS性能和轨迹平滑性，实现快速高质量的自主空中三维重建。
摘要：
面向三维重建的自主无人机路径规划已在各种高质量三维模型应用中得到积极研究。然而，大多数现有工作采用先探索后利用、基于先验或基于探索的策略，在重复飞行和低自主性方面表现出效率低下。在本文中，我们提出PredRecon，一个预测增强的规划框架，能够自主生成路径以获得高三维重建质量。我们从人类能够从部分观察中大致推断完整建筑结构这一能力中获得启发。因此，我们设计了一个表面预测模块（SPM），从当前部分重建中预测目标的粗略完整表面。然后，未覆盖的表面通过在线体素建图生成，等待无人机进行观测。最后，一个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，规划局部路径以最大化多视角立体（MVS）的性能，并生成平滑轨迹用于图像-位姿对的采集。我们在逼真模拟器中进行了基准测试，验证了PredRecon与经典方法和最先进方法相比的性能。开源代码发布在https://github.com/HKUST-Aerial-Robotics/PredRecon。
英文摘要：
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.
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。
