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 both tasks without requiring extensive prior data. However, in the case of inputting sparse Low Dynamic Range (LDR) panoramic images, NeRF often degrades with under-constrained geometry and is unable to reconstruct HDR radiance from LDR inputs. We observe that the radiance from each pixel in panoramic images can be modeled as both a signal to convey scene lighting information and a light source to illuminate other pixels. Hence, we propose the irradiance fields from sparse LDR panoramic images, which increases the observation counts for faithful geometry recovery and leverages the irradiance-radiance attenuation for HDR reconstruction. Extensive experiments demonstrate that the irradiance fields outperform state-of-the-art methods on both geometry recovery and HDR reconstruction and validate their effectiveness. Furthermore, we show a promising byproduct of spatially-varying lighting estimation. The code is available at https://github.com/Lu-Zhan/Pano-NeRF.

翻译：全景成像中的几何恢复和高动态范围重建研究随着扩展现实技术的发展成为趋势。神经辐射场为这两项任务提供了极具前景的场景表示方法，无需大量先验数据。然而，当输入稀疏低动态范围全景图像时，神经辐射场常因几何约束不足而退化，并无法从低动态范围输入重建高动态范围辐射度。我们观察到，全景图像中每个像素的辐射度既可视为传递场景光照信息的信号，也可视为照亮其他像素的光源。由此，我们提出基于稀疏低动态范围全景图像的辐照度场，该方法通过增加观测次数实现精确的几何恢复，并利用辐照度-辐射度衰减完成高动态范围重建。大量实验表明，该辐照度场在几何恢复和高动态范围重建两项任务上均优于现有方法，验证了其有效性。此外，我们展示了空间变化光照估计这一有益的副产品。代码开源见 https://github.com/Lu-Zhan/Pano-NeRF。