Towards Estimation of 3D Poses and Shapes of Animals from Oblique Drone Imagery

Abstract

Wildlife research in both terrestrial and aquatic ecosystems now deploys drone technology for tasks such as monitoring, census counts and habitat analysis. Unlike camera traps, drones offer real-time flexibility for adaptable flight paths and camera views, thus making them ideal for capturing multi-view data on wildlife like zebras or lions. With recent advancements in animals’ 3D shape & pose estimation, there is an increasing interest in bringing 3D analysis from ground to sky by means of drones. The paper reports some activities of the EU-funded WildDrone project and performs, for the first time, 3D analyses of animals exploiting oblique drone imagery. Using parametric model fitting, we estimate 3D shape and pose of animals from frames of a monocular RGB video. With the goal of appending metric information to parametric animal models using photogrammetric evidence, we propose a pipeline where we perform a point cloud reconstruction of the scene to scale and localize the animal within the 3D scene. Challenges, planned next steps and future directions are also reported.