逐步分支深度优化用于UAV林业应用中的DEFOM-Stereo和SAM3联合分析
Progressive Per-Branch Depth Optimization for DEFOM-Stereo and SAM3 Joint Analysis in UAV Forestry Applications
AI总结：
本文提出逐步分支深度优化方法，结合DEFOM-Stereo和SAM3，提升无人机林业中分支级3D重建精度与鲁棒性。
摘要：
准确的分支级3D重建是自主无人机树剪的先决条件；然而，现代立体匹配器生成的密集视差图往往仍过于嘈杂，无法用于复杂森林冠层中单个分支的分析。本文介绍了一种逐步流程，整合DEFOM-Stereo基础模型视差估计、SAM3实例分割和多阶段深度优化，以提供稳健的分支点云。从一个朴素的基线开始，我们系统地识别并解决三种错误类型，通过连续的改进。首先通过形态学侵蚀解决掩码边界污染，随后通过保留骨架的变体进行细化，以保护细长分支的拓扑结构。然后利用LAB空间Mahalanobis颜色验证结合跨分支重叠仲裁来缓解分割不准确的问题。最后，深度噪声——最持久的错误源——首先通过异常值去除和中值滤波减少，然后被一个包含MAD全局检测、空间密度共识、局部MAD滤波、RGB引导滤波和自适应双边滤波的稳健五阶段方案取代。在新西兰坎特伯雷地区，使用ZED Mini相机（63毫米基线）采集的1920x1080立体图像上评估，所提出的流程将平均分支深度标准差减少了82%，同时保持边缘保真度。结果是几何上一致的3D点云，适用于自主修剪工具定位。所有代码和处理数据均已公开发布，以促进进一步的无人机林业研究。
英文摘要：
Accurate per-branch 3D reconstruction is a prerequisite for autonomous UAV-based tree pruning; however, dense disparity maps from modern stereo matchers often remain too noisy for individual branch analysis in complex forest canopies. This paper introduces a progressive pipeline integrating DEFOM-Stereo foundation-model disparity estimation, SAM3 instance segmentation, and multi-stage depth optimization to deliver robust per-branch point clouds. Starting from a naive baseline, we systematically identify and resolve three error families through successive refinements. Mask boundary contamination is first addressed through morphological erosion and subsequently refined via a skeleton-preserving variant to safeguard thin-branch topology. Segmentation inaccuracy is then mitigated using LAB-space Mahalanobis color validation coupled with cross-branch overlap arbitration. Finally, depth noise - the most persistent error source - is initially reduced by outlier removal and median filtering, before being superseded by a robust five-stage scheme comprising MAD global detection, spatial density consensus, local MAD filtering, RGB-guided filtering, and adaptive bilateral filtering. Evaluated on 1920x1080 stereo imagery of Radiata pine (Pinus radiata) acquired with a ZED Mini camera (63 mm baseline) from a UAV in Canterbury, New Zealand, the proposed pipeline reduces the average per-branch depth standard deviation by 82% while retaining edge fidelity. The result is geometrically coherent 3D point clouds suitable for autonomous pruning tool positioning. All code and processed data are publicly released to facilitate further UAV forestry research.
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。
