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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.
|Title of host publication||Proceedings of 2021 IEEE/CVF International Conference on Computer Vision ICCV 2021|
|Number of pages||11|
|Publication status||Published - 28 Feb 2022|
|Event||International Conference on Computer Vision 2021 - Online|
Duration: 11 Oct 2021 → 17 Oct 2021
|Name||2021 IEEE/CVF International Conference on Computer Vision (ICCV)|
|Conference||International Conference on Computer Vision 2021|
|Abbreviated title||ICCV 2021|
|Period||11/10/21 → 17/10/21|
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Visual AI: An Open World Interpretable Visual Transformer
1/12/20 → 30/11/25