mlsanityData to 7 Oct 2026

wildNeRF: Complete view synthesis of in-the-wild dynamic scenes captured using sparse monocular data

arXiv 2209.10399 ↗First posted 20 Sep 2022cs.CVcited by 3

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Abstract (arXiv)

We present a novel neural radiance model that is trainable in a self-supervised manner for novel-view synthesis of dynamic unstructured scenes. Our end-to-end trainable algorithm learns highly complex, real-world static scenes within seconds and dynamic scenes with both rigid and non-rigid motion within minutes. By differentiating between static and motion-centric pixels, we create high-quality representations from a sparse set of images. We perform extensive qualitative and quantitative evaluation on existing benchmarks and set the state-of-the-art on performance measures on the challenging NVIDIA Dynamic Scenes Dataset. Additionally, we evaluate our model performance on challenging real-world datasets such as Cholec80 and SurgicalActions160.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 35 of its 58 references that are in the map. First posted on arXiv 20 Sep 2022.

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35 of 58 references.

References · 35

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23 more
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