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 this https URL

稀疏低动态范围（LDR）全景图像中辐照度场的模型提高了几何恢复和高动态范围（HDR）重建的能力，并且在实验证明了其有效性。

Pano-NeRF：从稀疏低动态范围全景图像合成具有几何图形的高动态范围新视点

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

High-quality panoramic images with a Field of View (FoV) of 360-degree are
essential for contemporary panoramic computer vision tasks. However,
conventional imaging systems come with sophisticated lens designs and heavy
optical components. This disqualifies their usage in many mobile and wearable
applications where thin and portable, minimalist imaging systems are desired.
In this paper, we propose a Panoramic Computational Imaging Engine (PCIE) to
address minimalist and high-quality panoramic imaging. With less than three
spherical lenses, a Minimalist Panoramic Imaging Prototype (MPIP) is
constructed based on the design of the Panoramic Annular Lens (PAL), but with
low-quality imaging results due to aberrations and small image plane size. We
propose two pipelines, i.e. Aberration Correction (AC) and Super-Resolution and
Aberration Correction (SR&AC), to solve the image quality problems of MPIP,
with imaging sensors of small and large pixel size, respectively. To provide a
universal network for the two pipelines, we leverage the information from the
Point Spread Function (PSF) of the optical system and design a PSF-aware
Aberration-image Recovery Transformer (PART), in which the self-attention
calculation and feature extraction are guided via PSF-aware mechanisms. We
train PART on synthetic image pairs from simulation and put forward the PALHQ
dataset to fill the gap of real-world high-quality PAL images for low-level
vision. A comprehensive variety of experiments on synthetic and real-world
benchmarks demonstrates the impressive imaging results of PCIE and the
effectiveness of plug-and-play PSF-aware mechanisms. We further deliver
heuristic experimental findings for minimalist and high-quality panoramic
imaging. Our dataset and code will be available at
this https URL

本文提出了全景计算成像引擎（PCIE）来解决高质量全景成像问题，利用自注意力计算和特征提取来设计 PSF 感知像差图像恢复变压器（PART），并在 MPIP 和 PALHQ 数据集实现了超分辨率和像差校正的两个流水线，从而在全景成像方面取得了令人印象深刻的成果。