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Panoramic imaging research on geometry recovery and High Dynamic Range (HDR)reconstruction becomes a trend with the development of Extended Reality (XR).Neural Radiance Fields (NeRF) provide a promising scene representation for bothtasks without requiring extensive prior data. However, in the case of inputtingsparse Low Dynamic Range (LDR) panoramic images, NeRF often degrades withunder-constrained geometry and is unable to reconstruct HDR radiance from LDRinputs. We observe that the radiance from each pixel in panoramic images can bemodeled as both a signal to convey scene lighting information and a lightsource to illuminate other pixels. Hence, we propose the irradiance fields fromsparse LDR panoramic images, which increases the observation counts forfaithful geometry recovery and leverages the irradiance-radiance attenuationfor HDR reconstruction. Extensive experiments demonstrate that the irradiancefields outperform state-of-the-art methods on both geometry recovery and HDRreconstruction and validate their effectiveness. Furthermore, we show apromising byproduct of spatially-varying lighting estimation. The code isavailable at https://github.com/Lu-Zhan/Pano-NeRF.

Pano-NeRF: Synthesizing High Dynamic Range Novel Views with Geometry from Sparse Low Dynamic Range Panoramic Images