WildLIFT: Lifting monocular drone video to 3D for species-agnostic wildlife monitoring
For ecologists and wildlife researchers, WildLIFT enables automated 3D detection and tracking from standard drone footage, reducing manual annotation effort and providing structured 3D metadata for behavioral and population studies.
WildLIFT lifts monocular drone video to 3D for species-agnostic wildlife monitoring, achieving high identity consistency in multi-animal scenes and substantially reducing manual 3D annotation effort through keyframe-based refinement, validated on 2,581 frames with over 6,700 3D detections across four large mammal species.
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.