ViewNeRF: Unsupervised Viewpoint Estimation Using Category-Level Neural Radiance Fields

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

Abstract / Description of output

We introduce ViewNeRF, a Neural Radiance Field-based viewpoint estimation method that learns to predict category-level viewpoints directly from images during training. While NeRF is usually trained with ground-truth camera poses, multiple extensions have been proposed to reduce the need for this expensive supervision. Nonetheless, most of these methods still struggle in complex settings with large camera movements, and are restricted to single scenes, i.e. they cannot be trained on a collection of scenes depicting the same object category. To address these issues, our method uses an analysis by synthesis approach, combining a conditional NeRF with a viewpoint predictor and a scene encoder in order to produce self-supervised reconstructions for whole object categories. Rather than focusing on high fidelity reconstruction, we target efficient and accurate viewpoint prediction in complex scenarios, e.g. 360° rotation on real data. Our model shows competitive results on synthetic and real datasets, both for single scenes and multi-instance collections.
Original languageEnglish
Title of host publicationProceedings of the 33rd British Machine Vision Conference
PublisherBMVA Press
Number of pages14
Publication statusPublished - 25 Nov 2022
EventThe 33rd British Machine Vision Conference, 2022 - London, United Kingdom
Duration: 21 Nov 202224 Nov 2022
Conference number: 33


ConferenceThe 33rd British Machine Vision Conference, 2022
Abbreviated titleBMVC 2022
Country/TerritoryUnited Kingdom
Internet address


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