mlsanityData to 7 Oct 2026

Casual Indoor HDR Radiance Capture from Omnidirectional Images

arXiv 2208.07903 ↗First posted 16 Aug 2022cs.CVcited by 11

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Abstract (arXiv)

We present PanoHDR-NeRF, a neural representation of the full HDR radiance field of an indoor scene, and a pipeline to capture it casually, without elaborate setups or complex capture protocols. First, a user captures a low dynamic range (LDR) omnidirectional video of the scene by freely waving an off-the-shelf camera around the scene. Then, an LDR2HDR network uplifts the captured LDR frames to HDR, which are used to train a tailored NeRF++ model. The resulting PanoHDR-NeRF can render full HDR images from any location of the scene. Through experiments on a novel test dataset of real scenes with the ground truth HDR radiance captured at locations not seen during training, we show that PanoHDR-NeRF predicts plausible HDR radiance from any scene point. We also show that the predicted radiance can synthesize correct lighting effects, enabling the augmentation of indoor scenes with synthetic objects that are lit correctly. Datasets and code are available at https://lvsn.github.io/PanoHDR-NeRF/.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 38 of its 60 references that are in the map. First posted on arXiv 16 Aug 2022.

Builds on

38 of 60 references.

References · 38

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26 more
  1. 2020NeRD: Neural Reflectance Decomposition from Image Collectionscited by 446
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  3. 2021Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughscited by 396
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Citing papers per year: 2022: 2; 2023: 3; 2024: 3; 2025: 3.

11 papers.