title: Orient Anything: Learning Robust Object Orientation Estimation from
    Rendering 3D Models
summary: Orientation is a key attribute of objects, crucial for understanding their
  spatial pose and arrangement in images. However, practical solutions for
  accurate orientation estimation from a single image remain underexplored. In
  this work, we introduce Orient Anything, the first expert and foundational
  model designed to estimate object orientation in a single- and free-view image.
  Due to the scarcity of labeled data, we propose extracting knowledge from the
  3D world. By developing a pipeline to annotate the front face of 3D objects and
  render images from random views, we collect 2M images with precise orientation
  annotations. To fully leverage the dataset, we design a robust training
  objective that models the 3D orientation as probability distributions of three
  angles and predicts the object orientation by fitting these distributions.
  Besides, we employ several strategies to improve synthetic-to-real transfer.
  Our model achieves state-of-the-art orientation estimation accuracy in both
  rendered and real images and exhibits impressive zero-shot ability in various
  scenarios. More importantly, our model enhances many applications, such as
  comprehension and generation of complex spatial concepts and 3D object pose
  adjustment.
paper_authors._id: 676bb2c29063304d2d9ec676
paper_authors.name: Zehan Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-30T20:48:11.548Z
paper_authors._id: 663b4d6aa55b0634634cd302
paper_authors.avatarUrl: /avatars/1191982568ad67895225f22844b6da99.svg
paper_authors.fullname: ZehanWang
paper_authors.type: user
paper_authors.user: ZehanWang
paper_authors._id: 676bb2c29063304d2d9ec677
paper_authors.name: Ziang Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-30T20:48:02.295Z
paper_authors._id: 66850e08a1bb72ba2d1d25a7
paper_authors.avatarUrl: /avatars/67b29e82c06812f987d2a65907de50ad.svg
paper_authors.fullname: Ziang Zhang
paper_authors.type: user
paper_authors.user: ziangzhang
paper_authors._id: 676bb2c29063304d2d9ec678
paper_authors.name: Tianyu Pang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-30T20:52:17.280Z
paper_authors._id: 63d91b6d255ef6add20e1b38
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1675921369867-63d91b6d255ef6add20e1b38.jpeg
paper_authors.fullname: Tianyu Pang
paper_authors.type: user
paper_authors.user: P2333
paper_authors._id: 676bb2c29063304d2d9ec679
paper_authors.name: Chao Du
paper_authors._id: 676bb2c29063304d2d9ec67a
paper_authors.name: Hengshuang Zhao
paper_authors._id: 676bb2c29063304d2d9ec67b
paper_authors.name: Zhou Zhao
paper_title: Orient Anything: Learning Robust Object Orientation Estimation from
    Rendering 3D Models
paper_summary: Orientation is a key attribute of objects, crucial for understanding their
  spatial pose and arrangement in images. However, practical solutions for
  accurate orientation estimation from a single image remain underexplored. In
  this work, we introduce Orient Anything, the first expert and foundational
  model designed to estimate object orientation in a single- and free-view image.
  Due to the scarcity of labeled data, we propose extracting knowledge from the
  3D world. By developing a pipeline to annotate the front face of 3D objects and
  render images from random views, we collect 2M images with precise orientation
  annotations. To fully leverage the dataset, we design a robust training
  objective that models the 3D orientation as probability distributions of three
  angles and predicts the object orientation by fitting these distributions.
  Besides, we employ several strategies to improve synthetic-to-real transfer.
  Our model achieves state-of-the-art orientation estimation accuracy in both
  rendered and real images and exhibits impressive zero-shot ability in various
  scenarios. More importantly, our model enhances many applications, such as
  comprehension and generation of complex spatial concepts and 3D object pose
  adjustment.
paper_ai_keywords: Orient Anything single- and free-view image 3D orientation probability distributions synthetic-to-real transfer zero-shot ability comprehension and generation of complex spatial concepts 3D object pose adjustment