  The purpose of intrinsic decomposition is to separate an image into its
albedo (reflective properties) and shading components (illumination
properties). This is challenging because it's an ill-posed problem.
Conventional approaches primarily concentrate on 2D imagery and fail to fully
exploit the capabilities of 3D data representation. 3D point clouds offer a
more comprehensive format for representing scenes, as they combine geometric
and color information effectively. To this end, in this paper, we introduce
Point Intrinsic Net (PoInt-Net), which leverages 3D point cloud data to
concurrently estimate albedo and shading maps. The merits of PoInt-Net include
the following aspects. First, the model is efficient, achieving consistent
performance across point clouds of any size with training only required on
small-scale point clouds. Second, it exhibits remarkable robustness; even when
trained exclusively on datasets comprising individual objects, PoInt-Net
demonstrates strong generalization to unseen objects and scenes. Third, it
delivers superior accuracy over conventional 2D approaches, demonstrating
enhanced performance across various metrics on different datasets. (Code
Released)
