Casual Indoor HDR Radiance Capture from Omnidirectional Images

• URL: http://arxiv.org/abs/2208.07903v1
• Date: Tue, 16 Aug 2022 18:45:27 GMT
• Title: Casual Indoor HDR Radiance Capture from Omnidirectional Images
• Authors: Pulkit Gera, Mohammad Reza Karimi Dastjerdi, Charles Renaud, P. J. Narayanan, Jean-Fran\c{c}ois Lalonde
• Abstract summary: We present Pano-NeRF, a novel pipeline to casually capture a plausible full HDR radiance field of a large indoor scene. The resulting Pano-NeRF pipeline can estimate full HDR panoramas from any location of the scene.
• Score: 6.558757117312684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present PanoHDR-NeRF, a novel pipeline to casually capture a plausible full HDR radiance field of a large indoor scene 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, subsequently used to train a tailored NeRF++ model. The resulting PanoHDR-NeRF pipeline can estimate full HDR panoramas from any location of the scene. Through experiments on a novel test dataset of a variety of real scenes with the ground truth HDR radiance captured at locations not seen during training, we show that PanoHDR-NeRF predicts plausible radiance from any scene point. We also show that the HDR images produced by PanoHDR-NeRF can synthesize correct lighting effects, enabling the augmentation of indoor scenes with synthetic objects that are lit correctly.

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