WildLIFT：将单目无人机视频提升到3D用于物种无关的野生动物监测
WildLIFT: Lifting monocular drone video to 3D for species-agnostic wildlife monitoring
AI总结：
WildLIFT通过整合单目无人机视频的3D场景几何与开放词汇2D实例分割，实现物种无关的3D检测与跟踪，提升野生动物监测的分析能力。
摘要：
单目RGB相机安装在无人机上广泛用于野生动物监测，但大多数分析流程仍局限于二维图像空间，使视频中的几何信息未被充分利用。我们提出了WildLIFT，一个计算框架，将单目无人机视频的三维场景几何与开放词汇2D实例分割相结合，以实现物种无关的3D检测和跟踪。带有语义面部信息的定向3D边界框标签能够对视角覆盖和动物间遮挡进行定量评估，生成结构化的元数据用于下游生态分析。我们在包含超过6700个3D检测的2581个手动整理的帧中验证了该框架，涵盖四种大型哺乳动物物种。WildLIFT在多动物场景中保持高身份一致性，并通过关键帧优化大幅减少手动3D标注工作量。通过将标准无人机视频转换为结构化的3D和视角感知表示，WildLIFT扩展了空中野生动物数据集在行为研究和种群监测中的分析用途。
英文摘要：
Monocular RGB cameras mounted on drones are widely used for wildlife monitoring, yet most analytical pipelines remain confined to two-dimensional image space, leaving geometric information in video underexploited. We present WildLIFT, a computational framework that integrates three-dimensional scene geometry from monocular drone video with open-vocabulary 2D instance segmentation to enable species-agnostic 3D detection and tracking. Oriented 3D bounding box labels with semantic face information enable quantitative assessment of viewpoint coverage and inter-animal occlusion, producing structured metadata for downstream ecological analyses. We validate the framework on 2,581 manually curated frames comprising over 6,700 3D detections across four large mammal species. WildLIFT maintains high identity consistency in multi-animal scenes and substantially reduces manual 3D annotation effort through keyframe-based refinement. By transforming standard drone footage into structured 3D and viewpoint-aware representations, WildLIFT extends the analytical utility of aerial wildlife datasets for behavioural research and population monitoring.
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。
