生态数据集，3D分割新基准
该研究发布了1.4 TB多模态无人机数据集，覆盖104棵含巢树木，包含27,945张RGB图像、111,780张多光谱图像及约7.81亿个3D点。语义分割基准测试中，Point Transformer V3在测试集上达到86.35% mIoU，优于KPConv和RandLA-Net。数据集结合光谱、空间与结构信息，可支持巢体积估计等生态应用，并为极端类别不平衡下的3D分割算法提供挑战性基准。
NEST3D: A High-Resolution Multimodal Dataset of Sociable Weaver Tree Nests
Sociable weaver nests function as complex ecological structures offering thermoregulatory microhabitats and sustaining diverse species; however, datasets used in prior studies lack fine-grained 3D structural detail. Producing usable and accurate 3D weaver nest data is challenging due to their irregular geometry and integration with complex host vegetation. We bridge this gap with an open-access, 1.4 TB multimodal drone dataset of 104 nest-bearing trees, comprising 27,945 RGB images, 111,780 multispectral images, approximately 781 million 3D points, and expert-annotated semantic segmentation labels. We benchmark semantic segmentation using KPConv, RandLA-Net, and Point Transformer V3, with PT-v3 achieving an mIoU of 86.35% on the test set. While the results demonstrate strong performance for transformer-based and point-wise methods, they also highlight architecture-dependent challenges, particularly for convolution-based approaches such as KPConv. By uniquely combining spectral, spatial, and structural information, the presented dataset advances 3D reconstruction, segmentation, and classification algorithms, enabling ecological applications from nest volume estimation to species conservation, and serves as a demanding benchmark that exposes architecture-dependent performance under extreme class imbalance.