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In this paper, we introduce a new challenge for synthesizing novel viewimages in practical environments with limited input multi-view images andvarying lighting conditions. Neural radiance fields (NeRF), one of thepioneering works for this task, demand an extensive set of multi-view imagestaken under constrained illumination, which is often unattainable in real-worldsettings. While some previous works have managed to synthesize novel viewsgiven images with different illumination, their performance still relies on asubstantial number of input multi-view images. To address this problem, wesuggest ExtremeNeRF, which utilizes multi-view albedo consistency, supported bygeometric alignment. Specifically, we extract intrinsic image components thatshould be illumination-invariant across different views, enabling directappearance comparison between the input and novel view under unconstrainedillumination. We offer thorough experimental results for task evaluation,employing the newly created NeRF Extreme benchmark-the first in-the-wildbenchmark for novel view synthesis under multiple viewing directions andvarying illuminations.

Few-shot Neural Radiance Fields Under Unconstrained Illumination