PanDORA: Casual HDR Radiance Acquisition for Indoor Scenes

Abstract

Most novel view synthesis methods-including Neural Radiance Fields (NeRF)-struggle to capture the true high dynamic range (HDR) radiance of scenes. This is primarily due to their dependence on low dynamic range (LDR) images from conventional cameras. Exposure bracketing techniques aim to address this challenge, but they introduce a considerable time burden during the acquisition process. In this work, we introduce PanDORA: PANoramic Dual-Observer Radiance Acquisition, a system designed for the casual, high quality HDR capture of indoor environments. Our approach uses two 360° cameras mounted on a portable monopod to simultaneously record two panoramic 360° videos: one with standard exposure and another at fast shutter speed. The resulting video data is processed by a proposed two-stage NeRF-based algorithm, including an algorithm for the fine alignment of the fast- and well-exposed frames, generating non-saturated HDR radiance maps. Compared to existing methods on a novel dataset of real indoor scenes captured with our apparatus and including HDR ground truth lighting, PanDORA achieves superior visual fidelity and provides a scalable solution for capturing real environments in HDR.