OccuFly：航空视角下语义场景补全的3D视觉基准
OccuFly: A 3D Vision Benchmark for Semantic Scene Completion from the Aerial Perspective
提供了首个真实世界、基于相机的航空视角语义场景补全基准，并揭示了当前视觉基础模型在航空场景下的根本性局限，为鲁棒3D场景理解研究提供了新挑战和评测平台。
原文依据 we introduce OccuFly, the first real-world, camera-based aerial SSC benchmark
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论语义场景补全（SSC）对于移动机器人的3D感知至关重要，因为它通过联合估计稠密体积占用和逐体素语义来实现整体场景理解。尽管SSC在自动驾驶等地面领域已被广泛研究，但自主飞行等航空场景在很大程度上仍未被探索，从而限制了下游应用的进展。此外，激光雷达传感器是SSC数据生成的主要模态，这对大多数无人机（UAV）构成了挑战，原因包括飞行法规、质量和能量限制，以及从高空视角获取的激光雷达点云的稀疏性。为解决这些限制，我们提出了一种无需激光雷达、基于相机的数据生成框架。通过利用经典3D重建，我们的框架将不到10%的标注图像提升到重建点云中，自动完成语义标签转移，从而大幅减少人工3D标注的工作量。基于该框架，我们推出了OccuFly，这是首个真实世界、基于相机的航空SSC基准，覆盖多个高度和所有季节。OccuFly提供了超过20,000个样本，包括图像、语义体素网格和度量深度图，涵盖城市、工业和乡村环境中的21个语义类别，并遵循既有的数据组织方式以便无缝集成。我们在OccuFly上对SSC和度量单目深度估计进行了基准测试，揭示了当前视觉基础模型在航空场景下的根本性局限，并为航空领域鲁棒的3D场景理解确立了新的挑战。访问 https://github.com/markus-42/occufly。
英文原文摘要 1663 字符
Semantic Scene Completion (SSC) is essential for 3D perception in mobile robotics, as it enables holistic scene understanding by jointly estimating dense volumetric occupancy and per-voxel semantics. Although SSC has been widely studied in terrestrial domains such as autonomous driving, aerial settings like autonomous flying remain largely unexplored, thereby limiting progress on downstream applications. Furthermore, LiDAR sensors are the primary modality for SSC data generation, which poses challenges for most uncrewed aerial vehicles (UAVs) due to flight regulations, mass and energy constraints, and the sparsity of LiDAR point clouds from elevated viewpoints. To address these limitations, we propose a LiDAR-free, camera-based data generation framework. By leveraging classical 3D reconstruction, our framework automates semantic label transfer by lifting <10% of annotated images into the reconstructed point cloud, substantially minimizing manual 3D annotation effort. Based on this framework, we introduce OccuFly, the first real-world, camera-based aerial SSC benchmark, captured across multiple altitudes and all seasons. OccuFly provides over 20,000 samples of images, semantic voxel grids, and metric depth maps across 21 semantic classes in urban, industrial, and rural environments, and follows established data organization for seamless integration. We benchmark both SSC and metric monocular depth estimation on OccuFly, revealing fundamental limitations of current vision foundation models in aerial settings and establishing new challenges for robust 3D scene understanding in the aerial domain. Visit https://github.com/markus-42/occufly.