Intrinsic Image Decomposition Using Point Cloud Representation
arxiv(2023)
摘要
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)
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