论文标题
点：移动增强现实的有效照明估算
PointAR: Efficient Lighting Estimation for Mobile Augmented Reality
论文作者
论文摘要
我们提出了一条有效的照明估计管道，该管道适合在现代移动设备上运行，具有可比的资源复杂性与最先进的移动深度学习模型。我们的管道PointAR采用了从移动摄像机捕获的单个RGB-D图像，并在该图像中捕获了2D位置，并估算了第二阶球形谐波系数。在增强现实的背景下，可以通过渲染引擎支持空间变体室内照明来直接利用这种估计的球形谐波系数。我们的主要见解是直接从点云中制定照明估计作为基于点云的学习问题，部分是受到由实时球形谐波照明利用的蒙特卡洛集成的启发。尽管现有方法通过复杂的深度学习管道估算照明信息，但我们的方法着重于降低计算复杂性。通过定量和定性实验，我们证明了与最新方法相比，PointAR达到了较低的照明估计误差。此外，我们的方法需要较低的资源级，与移动特异性DNN相当。
We propose an efficient lighting estimation pipeline that is suitable to run on modern mobile devices, with comparable resource complexities to state-of-the-art mobile deep learning models. Our pipeline, PointAR, takes a single RGB-D image captured from the mobile camera and a 2D location in that image, and estimates 2nd order spherical harmonics coefficients. This estimated spherical harmonics coefficients can be directly utilized by rendering engines for supporting spatially variant indoor lighting, in the context of augmented reality. Our key insight is to formulate the lighting estimation as a point cloud-based learning problem directly from point clouds, which is in part inspired by the Monte Carlo integration leveraged by real-time spherical harmonics lighting. While existing approaches estimate lighting information with complex deep learning pipelines, our method focuses on reducing the computational complexity. Through both quantitative and qualitative experiments, we demonstrate that PointAR achieves lower lighting estimation errors compared to state-of-the-art methods. Further, our method requires an order of magnitude lower resource, comparable to that of mobile-specific DNNs.
