ViewNet: Unsupervised Viewpoint Estimation from Conditional Generation

Octave Mariotti, Oisin Mac Aodha, Hakan Bilen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Understanding the 3D world without supervision is currently a major challenge in computer vision as the annotations required to supervise deep networks for tasks in this domain are expensive to obtain on a large scale. In this paper, we address the problem of unsupervised viewpoint estimation. We formulate this as a self-supervised learning task, where image reconstruction provides the supervision needed to predict the camera viewpoint. Specifically, we make use of pairs of images of the same object at training time, from unknown viewpoints, to self-supervise training by combining the viewpoint information from one image with the appearance information from the other. We demonstrate that using a perspective spatial transformer allows efficient viewpoint learning, outperforming existing unsupervised approaches on synthetic data, and obtains competitive results on the challenging PASCAL3D+ dataset.
Original languageEnglish
Title of host publicationInternational Conference on Computer Vision 2021
Number of pages11
Publication statusAccepted/In press - 22 Jul 2021
EventInternational Conference on Computer Vision 2021 - Online
Duration: 11 Oct 202117 Oct 2021


ConferenceInternational Conference on Computer Vision 2021
Abbreviated titleICCV 2021
Internet address


Dive into the research topics of 'ViewNet: Unsupervised Viewpoint Estimation from Conditional Generation'. Together they form a unique fingerprint.

Cite this