# Beyond the Pixel: a Photometrically Calibrated HDR Dataset for Luminance and Color Prediction arXiv: https://arxiv.org/abs/2304.12372 ยท HTML: https://mlsanity.com/paper/2304.12372/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 24 of its 75 references that are in the map. - First posted on arXiv 24 Apr 2023. ## Builds on (most cited first, 24 of 24) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images](https://mlsanity.com/paper/2111.13679/) (2021) - [Fixup Initialization: Residual Learning Without Normalization](https://mlsanity.com/paper/1901.09321/) (2019) - [Learning to Predict Indoor Illumination from a Single Image](https://mlsanity.com/paper/1704.00090/) (2017) - [Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline](https://mlsanity.com/paper/2004.01179/) (2020) - [HDR-NeRF: High Dynamic Range Neural Radiance Fields](https://mlsanity.com/paper/2111.14451/) (2021) - [ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content](https://mlsanity.com/paper/1803.02266/) (2018) - [Deep Parametric Indoor Lighting Estimation](https://mlsanity.com/paper/1910.08812/) (2019) - [HDR-GAN: HDR Image Reconstruction from Multi-Exposed LDR Images with Large Motions](https://mlsanity.com/paper/2007.01628/) (2020) - [Single Image HDR Reconstruction Using a CNN with Masked Features and Perceptual Loss](https://mlsanity.com/paper/2005.07335/) (2020) - [Deep Optics for Single-shot High-dynamic-range Imaging](https://mlsanity.com/paper/1908.00620/) (2019) - [Deep White-Balance Editing](https://mlsanity.com/paper/2004.01354/) (2020) - [Fast Fourier Color Constancy](https://mlsanity.com/paper/1611.07596/) (2016) - [What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network Performance](https://mlsanity.com/paper/1912.06960/) (2019) - [Convolutional Color Constancy](https://mlsanity.com/paper/1507.00410/) (2015) - [Learning High Dynamic Range from Outdoor Panoramas](https://mlsanity.com/paper/1703.10200/) (2017) - [Deep Chain HDRI: Reconstructing a High Dynamic Range Image from a Single Low Dynamic Range Image](https://mlsanity.com/paper/1801.06277/) (2018) - [Auto White-Balance Correction for Mixed-Illuminant Scenes](https://mlsanity.com/paper/2109.08750/) (2021) - [Learning sRGB-to-Raw-RGB De-rendering with Content-Aware Metadata](https://mlsanity.com/paper/2206.01813/) (2022) - [Luminance Attentive Networks for HDR Image and Panorama Reconstruction](https://mlsanity.com/paper/2109.06688/) (2021) - [High Dynamic Range and Super-Resolution from Raw Image Bursts](https://mlsanity.com/paper/2207.14671/) (2022) - [Casual Indoor HDR Radiance Capture from Omnidirectional Images](https://mlsanity.com/paper/2208.07903/) (2022) - [Beyond Visual Attractiveness: Physically Plausible Single Image HDR Reconstruction for Spherical Panoramas](https://mlsanity.com/paper/2103.12926/) (2021) ## Built on by (most cited first, 14 of 14) - [GeoCalib: Learning Single-image Calibration with Geometric Optimization](https://mlsanity.com/paper/2409.06704/) (2024) - [LEDiff: Latent Exposure Diffusion for HDR Generation](https://mlsanity.com/paper/2412.14456/) (2024) - [Thinking with Camera: A Unified Multimodal Model for Camera-Centric Understanding and Generation](https://mlsanity.com/paper/2510.08673/) (2025) - [AnyCalib: On-Manifold Learning for Model-Agnostic Single-View Camera Calibration](https://mlsanity.com/paper/2503.12701/) (2025) - [GaSLight: Gaussian Splats for Spatially-Varying Lighting in HDR](https://mlsanity.com/paper/2504.10809/) (2025) - [Diffusion Reflectance Map: Single-Image Stochastic Inverse Rendering of Illumination and Reflectance](https://mlsanity.com/paper/2312.04529/) (2023) - [Personalized Video Relighting With an At-Home Light Stage](https://mlsanity.com/paper/2311.08843/) (2023) - [X2HDR: HDR Image Generation in a Perceptually Uniform Space](https://mlsanity.com/paper/2602.04814/) (2026) - [Towards a Perceptual Evaluation Framework for Lighting Estimation](https://mlsanity.com/paper/2312.04334/) (2023) - [360U-Former: HDR Illumination Estimation with Panoramic Adapted Vision Transformers](https://mlsanity.com/paper/2410.13566/) (2024) - [CleAR: Robust Context-Guided Generative Lighting Estimation for Mobile Augmented Reality](https://mlsanity.com/paper/2411.02179/) (2024) - [Real-Time Scene-Adaptive Tone Mapping for High-Dynamic Range Object Detection](https://mlsanity.com/paper/2608.30400/) (2026) - [Recurrent Dynamic Range Extension](https://mlsanity.com/paper/2609.13135/) (2026) - [ExpandDiff: Dynamic Range Expanding Diffusion for Single-Image HDR Reconstruction](https://mlsanity.com/paper/2609.39624/) (2026) ## Abstract (arXiv) Light plays an important role in human well-being. However, most computer vision tasks treat pixels without considering their relationship to physical luminance. To address this shortcoming, we introduce the Laval Photometric Indoor HDR Dataset, the first large-scale photometrically calibrated dataset of high dynamic range 360deg panoramas. Our key contribution is the calibration of an existing, uncalibrated HDR Dataset. We do so by accurately capturing RAW bracketed exposures simultaneously with a professional photometric measurement device (chroma meter) for multiple scenes across a variety of lighting conditions. Using the resulting measurements, we establish the calibration coefficients to be applied to the HDR images. The resulting dataset is a rich representation of indoor scenes which displays a wide range of illuminance and color, and varied types of light sources. We exploit the dataset to introduce three novel tasks, where: per-pixel luminance, per-pixel color and planar illuminance can be predicted from a single input image. Finally, we also capture another smaller photometric dataset with a commercial 360deg camera, to experiment on generalization across cameras. We are optimistic that the release of our datasets and associated code will spark interest in physically accurate light estimation within the community. Dataset and code are available at https://lvsn.github.io/beyondthepixel/.