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

• URL: http://arxiv.org/abs/2209.10399v1
• Date: Tue, 20 Sep 2022 14:37:56 GMT
• Title: wildNeRF: Complete view synthesis of in-the-wild dynamic scenes captured using sparse monocular data
• Authors: Shuja Khalid, Frank Rudzicz
• Abstract summary: 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.
• Score: 16.7345472998388
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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