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arXiv 2510.09367cs.CV

Minkowski-MambaNet：一种用于森林生物量量化的选择性状态空间模型点云框架

Minkowski-MambaNet: A Point Cloud Framework with Selective State Space Models for Forest Biomass Quantification

School of Computer Science and Technology, the Key Laboratory of Signal Detection and Processing, and the Xinjiang Multimodal Intelligent Processing and Information Security Engineering Technology Research Center, Xinjiang University（计算机科学与技术学院、信号检测与处理重点实验室以及新疆多模态智能处理与信息安全工程技术研究中心，新疆大学）；National Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家实验室，南京大学）；Xinjiang Guanghui New Energy Co., Ltd（新疆广汇新能源有限公司）；Hongyousoft Co., Ltd（宏远软件有限公司）

Jinxiang Tu, Dayong Ren, Fei Shi, Zhenhong Jia, Yahong Ren, Jiwei Qin, Fang He

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AI总结：

提出Minkowski-MambaNet，将选择性状态空间模型集成到Minkowski网络中，直接从LiDAR点云估算森林体积和地上生物量，在丹麦国家森林数据上优于现有方法，且无需DTM，对边界伪影鲁棒。

摘要：

准确的森林生物量量化对于碳循环监测至关重要。虽然机载LiDAR在捕捉三维森林结构方面表现出色，但由于难以建模区分树木所需的长距离依赖关系，直接从点云估算木质体积和地上生物量（AGB）具有挑战性。我们提出了Minkowski-MambaNet，一种新颖的深度学习框架，可直接从原始LiDAR数据估算体积和AGB。其关键创新在于将Mamba模型的选择性状态空间模型（SSM）集成到Minkowski网络中，从而能够有效编码全局上下文和长距离依赖关系，以改进树木区分。同时加入了跳跃连接以增强特征并加速收敛。在丹麦国家森林清查LiDAR数据上的评估表明，Minkowski-MambaNet显著优于最先进的方法，提供了更准确和稳健的估算结果。至关重要的是，它不需要数字地形模型（DTM），并且对边界伪影具有鲁棒性。这项工作为大规模森林生物量分析提供了强大工具，推进了基于LiDAR的森林清查。

英文摘要：

Accurate forest biomass quantification is vital for carbon cycle monitoring. While airborne LiDAR excels at capturing 3D forest structure, directly estimating woody volume and Aboveground Biomass (AGB) from point clouds is challenging due to difficulties in modeling long-range dependencies needed to distinguish trees.We propose Minkowski-MambaNet, a novel deep learning framework that directly estimates volume and AGB from raw LiDAR. Its key innovation is integrating the Mamba model's Selective State Space Model (SSM) into a Minkowski network, enabling effective encoding of global context and long-range dependencies for improved tree differentiation. Skip connections are incorporated to enhance features and accelerate convergence.Evaluated on Danish National Forest Inventory LiDAR data, Minkowski-MambaNet significantly outperforms state-of-the-art methods, providing more accurate and robust estimates. Crucially, it requires no Digital Terrain Model (DTM) and is robust to boundary artifacts. This work offers a powerful tool for large-scale forest biomass analysis, advancing LiDAR-based forest inventories.

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。

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