View source for US Patent Application 17827019. WARPING DEPTH FEATURES FOR DEPTH ESTIMATION simplified abstract

=WARPING DEPTH FEATURES FOR DEPTH ESTIMATION=


==Organization Name==

[[:Category:TOYOTA JIDOSHA KABUSHIKI KAISHA|TOYOTA JIDOSHA KABUSHIKI KAISHA]]


[[Category:TOYOTA JIDOSHA KABUSHIKI KAISHA]]

==Inventor(s)==

[[:Category:Vitor Guizilini of Santa Clara CA (US)|Vitor Guizilini of Santa Clara CA (US)]][[Category:Vitor Guizilini of Santa Clara CA (US)]]

[[:Category:Rares A. Ambrus of San Francisco CA (US)|Rares A. Ambrus of San Francisco CA (US)]][[Category:Rares A. Ambrus of San Francisco CA (US)]]

[[:Category:Sergey Zakharov of San Francisco CA (US)|Sergey Zakharov of San Francisco CA (US)]][[Category:Sergey Zakharov of San Francisco CA (US)]]

==WARPING DEPTH FEATURES FOR DEPTH ESTIMATION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17827019 titled 'WARPING DEPTH FEATURES FOR DEPTH ESTIMATION

==Simplified Explanation==

- The patent application describes an improved approach to training a depth model for monocular depth estimation.
- The method involves encoding a source image into depth features using an encoder of the depth model.
- The depth features are then warped into warped features of a target frame of a target image associated with the source image.
- The warped features are decoded into a depth map using a decoder of the depth model.
- The depth model is trained based on a loss derived from the depth map.


==Original Abstract Submitted==

System, methods, and other embodiments described herein relate to an improved approach to training a depth model for monocular depth estimation by warping depth features prior to decoding. In one embodiment, a method includes encoding, using an encoder of a depth model, a source image into depth features of a scene depicted by the source image. The method includes warping the depth features into warped features of a target frame of a target image associated with the source image. The method includes decoding, using a decoder of the depth model, the warped features into a depth map. The method includes training the depth model according to a loss derived from the depth map.



[[Category:G06T7/579]]
[[Category:G06T3/00]]
[[Category:G06N20/00]]