title: Temporally-consistent 3D Reconstruction of Birds
summary: This paper deals with 3D reconstruction of seabirds which recently came into
  focus of environmental scientists as valuable bio-indicators for environmental
  change. Such 3D information is beneficial for analyzing the bird's behavior and
  physiological shape, for example by tracking motion, shape, and appearance
  changes. From a computer vision perspective birds are especially challenging
  due to their rapid and oftentimes non-rigid motions. We propose an approach to
  reconstruct the 3D pose and shape from monocular videos of a specific breed of
  seabird - the common murre. Our approach comprises a full pipeline of
  detection, tracking, segmentation, and temporally consistent 3D reconstruction.
  Additionally, we propose a temporal loss that extends current single-image 3D
  bird pose estimators to the temporal domain. Moreover, we provide a real-world
  dataset of 10000 frames of video observations on average capture nine birds
  simultaneously, comprising a large variety of motions and interactions,
  including a smaller test set with bird-specific keypoint labels. Using our
  temporal optimization, we achieve state-of-the-art performance for the
  challenging sequences in our dataset.
paper_authors._id: 66cf099c59013592dde34887
paper_authors.name: Johannes Hägerlind
paper_authors._id: 66cf099c59013592dde34888
paper_authors.name: Jonas Hentati-Sundberg
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-08-28T12:35:19.146Z
paper_authors._id: 642085fb69a2c293387fc661
paper_authors.avatarUrl: /avatars/72935ec35b43fb01ab1e6d0b03ba520f.svg
paper_authors.fullname: Jonas Hentati Sundberg
paper_authors.type: user
paper_authors.user: jonhensun
paper_authors._id: 66cf099c59013592dde34889
paper_authors.name: Bastian Wandt
paper_title: Temporally-consistent 3D Reconstruction of Birds
paper_summary: This paper deals with 3D reconstruction of seabirds which recently came into
  focus of environmental scientists as valuable bio-indicators for environmental
  change. Such 3D information is beneficial for analyzing the bird's behavior and
  physiological shape, for example by tracking motion, shape, and appearance
  changes. From a computer vision perspective birds are especially challenging
  due to their rapid and oftentimes non-rigid motions. We propose an approach to
  reconstruct the 3D pose and shape from monocular videos of a specific breed of
  seabird - the common murre. Our approach comprises a full pipeline of
  detection, tracking, segmentation, and temporally consistent 3D reconstruction.
  Additionally, we propose a temporal loss that extends current single-image 3D
  bird pose estimators to the temporal domain. Moreover, we provide a real-world
  dataset of 10000 frames of video observations on average capture nine birds
  simultaneously, comprising a large variety of motions and interactions,
  including a smaller test set with bird-specific keypoint labels. Using our
  temporal optimization, we achieve state-of-the-art performance for the
  challenging sequences in our dataset.
paper_ai_keywords: 3D reconstruction 3D pose 3D shape monocular videos detection tracking segmentation temporally consistent 3D reconstruction temporal loss temporal domain single-image 3D bird pose estimators real-world dataset video observations keypoint labels temporal optimization