Orient Anything: Learning Robust Object Orientation Estimation from
  Rendering 3D Models
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.
676bb2c29063304d2d9ec676
Zehan Wang
admin_assigned
2024-12-30T20:48:11.548Z
663b4d6aa55b0634634cd302
/avatars/1191982568ad67895225f22844b6da99.svg
ZehanWang
user
ZehanWang
676bb2c29063304d2d9ec677
Ziang Zhang
admin_assigned
2024-12-30T20:48:02.295Z
66850e08a1bb72ba2d1d25a7
/avatars/67b29e82c06812f987d2a65907de50ad.svg
Ziang Zhang
user
ziangzhang
676bb2c29063304d2d9ec678
Tianyu Pang
admin_assigned
2024-12-30T20:52:17.280Z
63d91b6d255ef6add20e1b38
https://cdn-avatars.huggingface.co/v1/production/uploads/1675921369867-63d91b6d255ef6add20e1b38.jpeg
Tianyu Pang
user
P2333
676bb2c29063304d2d9ec679
Chao Du
676bb2c29063304d2d9ec67a
Hengshuang Zhao
676bb2c29063304d2d9ec67b
Zhou Zhao
Orient Anything: Learning Robust Object Orientation Estimation from
  Rendering 3D Models
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.
Orient Anything model estimates object orientation from single images using 3D-world knowledge and achieves state-of-the-art accuracy with zero-shot abilities across various applications.
Orient Anything orientation estimation single-view free-view 3D orientation probability distributions zero-shot ability synthetic-to-real transfer