HDR-NeRF: High Dynamic Range Neural Radiance Fields

Huang, Xin; Zhang, Qi; Ye, Feng; Li, Hongdong; Wang, Xuan; Wang, Qing

doi:10.48550/arxiv.2111.14451

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Implicit Neural Representations1

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2022

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“…Recent work uses this property to model HDR scenes [27,39] and even estimate a physically-based camera sensor model [50]. We also apply photometric self-calibration in this work, but require a different sensor model that fits to typical X-ray detectors.…”

Section: Implicit Neural Representationsmentioning

confidence: 99%

NeAT

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Wang

et al. 2022

ACM Trans. Graph.

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In this paper, we present Neural Adaptive Tomography (NeAT), the first adaptive, hierarchical neural rendering pipeline for tomography. Through a combination of neural features with an adaptive explicit representation, we achieve reconstruction times far superior to existing neural inverse rendering methods. The adaptive explicit representation improves efficiency by facilitating empty space culling and concentrating samples in complex regions, while the neural features act as a neural regularizer for the 3D reconstruction. The NeAT framework is designed specifically for the tomographic setting, which consists only of semi-transparent volumetric scenes instead of opaque objects. In this setting, NeAT outperforms the quality of existing optimization-based tomography solvers while being substantially faster. https://github.com/darglein/NeAT

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Section: Implicit Neural Representationsmentioning

confidence: 99%