Modern 3D visual learning relies on observations sampled from metric 3D assets, yet existing scans, meshes, point clouds, simulations, and reconstructions do not directly provide a sparse, comparable, and geometry-consistent panoramic training interface. Dense trajectories duplicate nearby views, source-specific rendering policies yield heterogeneous annotations, and sparse heuristics may miss important regions or introduce depth-inconsistent observations. We study how to convert 3D assets into sparse panoramic RGB-D-pose data that preserves complete scene coverage with low redundancy and auditable provenance. We propose COVER (Coverage-Oriented Viewpoint curation with ERP Range-depth warping), a training-free ERP viewpoint curator that projects geometry observed from selected views into candidate ERP probes, scores incremental coverage, and penalizes depth conflicts. Under bounded proxy error, its greedy coverage proxy preserves the standard coverage-style approximation behavior up to an additive error term. Using COVER, we build CM-EVS (Coverage-curated Metric ERP View Set), a panoramic RGB-D-pose dataset with 36,373 curated ERP frames from 1,275 indoor scenes across Blender indoor, HM3D, and ScanNet++, complemented by outdoor panoramas from TartanGround and OB3D re-encoded into the same schema. Each frame provides full-sphere RGB, metric range depth, calibrated pose; COVER-produced indoor frames include per-step provenance logs. With a median of only 25 frames per indoor scene, CM-EVS covers all 13 unified room types while maintaining compact scene-level coverage. Experiments show that COVER improves the coverage-conflict trade-off, making CM-EVS a sparse, compact, and auditable RGB-D-pose resource for geometry-consistent panoramic 3D learning.

翻译：[translated abstract in Chinese] 现代三维视觉学习依赖于从度量三维资产中采样的观测数据，然而现有扫描、网格、点云、仿真和重建方法无法直接提供稀疏、可比且几何一致的全景训练接口。稠密轨迹导致近邻视角重复采样，源特定渲染策略产生异构标注，而稀疏启发式方法可能遗漏关键区域或引入深度不一致的观测。本文研究如何将三维资产转化为具有低冗余度和可审计来源的稀疏全景RGB-D-位姿数据，以保持完整场景覆盖。我们提出COVER（基于ERP范围深度变形的覆盖导向视点筛选方法），这是一种免训练的ERP视点筛选器，通过将选定视点观测到的几何投影至候选ERP探针，对增量覆盖进行评分，并对深度冲突施加惩罚。在有界代理误差下，其贪心覆盖代理能以附加误差项的形式保持标准覆盖式近似行为。基于COVER，我们构建了CM-EVS（覆盖筛选度量ERP视点集），包含来自Blender室内、HM3D和ScanNet++共1275个室内场景的36373个经筛选的ERP帧，以及通过相同架构重新编码的TartanGround和OB3D室外全景数据。每个帧提供全球面RGB、度量范围深度及标定位姿；COVER生成的室内帧包含逐步来源日志。CM-EVS室内场景中位数仅25帧，在覆盖所有13种统一房间类型的同时保持紧凑的场景级覆盖。实验表明，COVER优化了覆盖-冲突权衡，使CM-EVS成为几何一致全景三维学习中稀疏、紧凑且可审计的RGB-D-位姿资源。