# Pano-NeRF: Synthesizing High Dynamic Range Novel Views with Geometry from Sparse Low Dynamic Range Panoramic Images arXiv: https://arxiv.org/abs/2312.15942 ยท HTML: https://mlsanity.com/paper/2312.15942/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 32 of its 54 references that are in the map. - First posted on arXiv 26 Dec 2023. ## Builds on (most cited first, 30 of 32) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018) - [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021) - [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021) - [The Replica Dataset: A Digital Replica of Indoor Spaces](https://mlsanity.com/paper/1906.05797/) (2019) - [Depth-supervised NeRF: Fewer Views and Faster Training for Free](https://mlsanity.com/paper/2107.02791/) (2021) - [NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis](https://mlsanity.com/paper/2012.03927/) (2020) - [Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis](https://mlsanity.com/paper/2104.00677/) (2021) - [NeRD: Neural Reflectance Decomposition from Image Collections](https://mlsanity.com/paper/2012.03918/) (2020) - [NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images](https://mlsanity.com/paper/2111.13679/) (2021) - [FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization](https://mlsanity.com/paper/2303.07418/) (2023) - [Dense Depth Priors for Neural Radiance Fields from Sparse Input Views](https://mlsanity.com/paper/2112.03288/) (2021) - [Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Image](https://mlsanity.com/paper/1905.02722/) (2019) - [Neural Reflectance Fields for Appearance Acquisition](https://mlsanity.com/paper/2008.03824/) (2020) - [Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes](https://mlsanity.com/paper/2104.06935/) (2021) - [Modeling Indirect Illumination for Inverse Rendering](https://mlsanity.com/paper/2204.06837/) (2022) - [Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline](https://mlsanity.com/paper/2004.01179/) (2020) - [DiffusioNeRF: Regularizing Neural Radiance Fields with Denoising Diffusion Models](https://mlsanity.com/paper/2302.12231/) (2023) - [NeILF: Neural Incident Light Field for Physically-based Material Estimation](https://mlsanity.com/paper/2203.07182/) (2022) - [Learning Indoor Inverse Rendering with 3D Spatially-Varying Lighting](https://mlsanity.com/paper/2109.06061/) (2021) - [Single Image HDR Reconstruction Using a CNN with Masked Features and Perceptual Loss](https://mlsanity.com/paper/2005.07335/) (2020) - [Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination](https://mlsanity.com/paper/2003.08367/) (2020) - [OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware Fusion](https://mlsanity.com/paper/2203.00838/) (2022) - [HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset](https://mlsanity.com/paper/2103.14943/) (2021) - [HDR-VDP-3: A multi-metric for predicting image differences, quality and contrast distortions in high dynamic range and regular content](https://mlsanity.com/paper/2304.13625/) (2023) - [360SD-Net: 360deg Stereo Depth Estimation with Learnable Cost Volume](https://mlsanity.com/paper/1911.04460/) (2019) - [HDR-Plenoxels: Self-Calibrating High Dynamic Range Radiance Fields](https://mlsanity.com/paper/2208.06787/) (2022) - [ACDNet: Adaptively Combined Dilated Convolution for Monocular Panorama Depth Estimation](https://mlsanity.com/paper/2112.14440/) (2021) - [360FusionNeRF: Panoramic Neural Radiance Fields with Joint Guidance](https://mlsanity.com/paper/2209.14265/) (2022) - [Luminance Attentive Networks for HDR Image and Panorama Reconstruction](https://mlsanity.com/paper/2109.06688/) (2021) ## Built on by (most cited first, 7 of 7) - [AE-NeRF: Augmenting Event-Based Neural Radiance Fields for Non-ideal Conditions and Larger Scene](https://mlsanity.com/paper/2501.02807/) (2025) - [Simple-RF: Regularizing Sparse Input Radiance Fields with Simpler Solutions](https://mlsanity.com/paper/2404.19015/) (2024) - [A Cycle Ride to HDR: Semantics Aware Self-Supervised Framework for Unpaired LDR-to-HDR Image Reconstruction](https://mlsanity.com/paper/2410.15068/) (2024) - [PhysHDR: When Lighting Meets Materials and Scene Geometry in HDR Reconstruction](https://mlsanity.com/paper/2509.16869/) (2025) - [InstantHDR: Single-forward Gaussian Splatting Initialization for HDR 3D Reconstruction](https://mlsanity.com/paper/2603.11298/) (2026) - [FastPano3D: Feed-Forward Indoor Panoramic 3D Reconstruction from a Single Image](https://mlsanity.com/paper/2606.30352/) (2026) - [Unified Panoramic-Gaussian Representation for Monocular 4D Scene Synthesis](https://mlsanity.com/paper/2607.01663/) (2026) ## Abstract (arXiv) 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.