Intrinsic decomposition is to infer the albedo and shading from the image. Since it is a heavily ill-posed problem, previous methods rely on prior assumptions from 2D images, however, the exploration of the data representation itself is limited. The point cloud is known as a rich format of scene representation, which naturally aligns the geometric information and the color information of an image. Our proposed method, Point Intrinsic Net, in short, PoInt-Net, jointly predicts the albedo, light source direction, and shading, using point cloud representation. Experiments reveal the benefits of PoInt-Net, in terms of accuracy, it outperforms 2D representation approaches on multiple metrics across datasets; in terms of efficiency, it trains on small-scale point clouds and performs stably on any-scale point clouds; in terms of robustness, it only trains on single object level dataset, and demonstrates reasonable generalization ability for unseen objects and scenes.

翻译：本征分解旨在从图像中推断出反照率和光照。由于这是一个严重病态的问题，以往的方法依赖于二维图像中的先验假设，然而对数据表示本身的探索较为有限。点云作为一种丰富的场景表示形式，能够自然地对齐图像的几何信息与颜色信息。我们提出的方法——Point Intrinsic Net（简称PoInt-Net）——利用点云表示联合预测反照率、光源方向和光照。实验揭示了PoInt-Net的优势：在准确性方面，它在多个数据集上的多维度指标均优于二维表示方法；在效率方面，它可在小规模点云上训练，并稳定适用于任意规模的点云；在鲁棒性方面，它仅在单物体级别数据集上训练，便能对未见物体和场景展现出合理的泛化能力。