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We present PanoHDR-NeRF, a neural representation of the full HDR radiancefield of an indoor scene, and a pipeline to capture it casually, withoutelaborate setups or complex capture protocols. First, a user captures a lowdynamic range (LDR) omnidirectional video of the scene by freely waving anoff-the-shelf camera around the scene. Then, an LDR2HDR network uplifts thecaptured 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 thescene. Through experiments on a novel test dataset of real scenes with theground truth HDR radiance captured at locations not seen during training, weshow that PanoHDR-NeRF predicts plausible HDR radiance from any scene point. Wealso show that the predicted radiance can synthesize correct lighting effects,enabling the augmentation of indoor scenes with synthetic objects that are litcorrectly. Datasets and code are available athttps://lvsn.github.io/PanoHDR-NeRF/.

Casual Indoor HDR Radiance Capture from Omnidirectional Images