运动中的光照：时空高动态范围光照估计
Lighting in Motion: Spatiotemporal HDR Lighting Estimation
AI总结：
提出基于扩散的时空光照估计方法LiMo，通过生成不同曝光下的镜面与漫反射球体，结合深度与几何条件，实现高精度高频细节预测与照度估计。
摘要：
我们提出LiMo（运动中的光照），一种基于扩散的时空光照估计方法。LiMo旨在同时实现逼真的高频细节预测和准确的照度估计。为此，我们提出根据输入中3D位置生成一组不同曝光下的镜面与漫反射球体。利用扩散先验，我们在大规模定制的室内外场景数据集上微调强大的现有扩散模型，并配以时空光照探针。为了实现准确的空间条件，我们证明仅靠深度是不够的，并引入一种新的几何条件来提供场景相对于目标3D位置的相对位置。最后，我们利用可微渲染将不同曝光下的漫反射和镜面预测合并为单个HDRI图。我们彻底评估了我们的方法和设计选择，使LiMo在空间控制和预测精度方面均达到最先进水平。
英文摘要：
We present Lighting in Motion (LiMo), a diffusion-based approach to spatiotemporal lighting estimation. LiMo targets both realistic high-frequency detail prediction and accurate illuminance estimation. To account for both, we propose generating a set of mirrored and diffuse spheres at different exposures, based on their 3D positions in the input. Making use of diffusion priors, we fine-tune powerful existing diffusion models on a large-scale customized dataset of indoor and outdoor scenes, paired with spatiotemporal light probes. For accurate spatial conditioning, we demonstrate that depth alone is insufficient and we introduce a new geometric condition to provide the relative position of the scene to the target 3D position. Finally, we combine diffuse and mirror predictions at different exposures into a single HDRI map leveraging differentiable rendering. We thoroughly evaluate our method and design choices to establish LiMo as state-of-the-art for both spatial control and prediction accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
