PanDORA: Casual HDR Radiance Acquisition for Indoor Scenes

• URL: http://arxiv.org/abs/2407.06150v1
• Date: Mon, 8 Jul 2024 17:22:27 GMT
• Title: PanDORA: Casual HDR Radiance Acquisition for Indoor Scenes
• Authors: Mohammad Reza Karimi Dastjerdi, Frédéric Fortier-Chouinard, Yannick Hold-Geoffroy, Marc Hébert, Claude Demers, Nima Kalantari, Jean-François Lalonde,
• Abstract summary: We present PanDORA: a PANoramic Dual-Observer Radiance Acquisition system for the casual capture of indoor scenes in high dynamic range. Our proposed system comprises two 360deg cameras rigidly attached to a portable tripod. The cameras simultaneously acquire two 360deg videos: one at a regular exposure and the other at a very fast exposure. The resulting images are fed to a NeRF-based algorithm that reconstructs the scene's full high dynamic range.
• Score: 13.790885617434197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most novel view synthesis methods such as NeRF are unable to capture the true high dynamic range (HDR) radiance of scenes since they are typically trained on photos captured with standard low dynamic range (LDR) cameras. While the traditional exposure bracketing approach which captures several images at different exposures has recently been adapted to the multi-view case, we find such methods to fall short of capturing the full dynamic range of indoor scenes, which includes very bright light sources. In this paper, we present PanDORA: a PANoramic Dual-Observer Radiance Acquisition system for the casual capture of indoor scenes in high dynamic range. Our proposed system comprises two 360{\deg} cameras rigidly attached to a portable tripod. The cameras simultaneously acquire two 360{\deg} videos: one at a regular exposure and the other at a very fast exposure, allowing a user to simply wave the apparatus casually around the scene in a matter of minutes. The resulting images are fed to a NeRF-based algorithm that reconstructs the scene's full high dynamic range. Compared to HDR baselines from previous work, our approach reconstructs the full HDR radiance of indoor scenes without sacrificing the visual quality while retaining the ease of capture from recent NeRF-like approaches.

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