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3D Bird Reconstruction: a Dataset, Model, and Shape Recovery from a Single View
Marc Badger1, Yufu Wang1, Adarsh Modh1
1University of Pennsylvania, Philadelphia PA 19104, USA.
Computer Vision - ECCV ... : ... European Conference on Computer Vision : Proceedings. European Conference on Computer Vision
|July 13, 2022
Summary
Researchers developed a new method for accurately tracking bird pose and shape, overcoming challenges like occlusion in social settings. This advance aids neuroscience and social behavior studies.
Area of Science:
- Ethology
- Computer Vision
- Computational Neuroscience
Background:
- Automated animal pose estimation is crucial for studying animal behavior and neuroscience.
- Current methods struggle with occluded social animals like birds, limiting research scope.
Purpose of the Study:
- To develop a robust method for capturing avian pose and shape, addressing occlusion challenges.
- To enable accurate recovery of bird postures from single-view images.
Main Methods:
- Introduced a novel model and multi-view optimization approach for avian pose and shape.
- Developed a pipeline for keypoint, mask, pose, and shape regression from single views.
- Collected multi-view keypoint and mask annotations from 15 social birds in an outdoor aviary.
Main Results:
- Successfully captured the unique shape and pose space of live birds.
- Recovered accurate avian postures from single-view estimations.
- Created the extensive Penn Aviary Dataset for future research.
Conclusions:
- The new method significantly improves automated pose estimation for social birds.
- This work provides valuable tools and datasets for advancing avian behavior and neuroscience research.
