Intrinsic Image Decomposition Using Point Cloud Representation

• URL: http://arxiv.org/abs/2307.10924v2
• Date: Thu, 28 Mar 2024 09:54:38 GMT
• Title: Intrinsic Image Decomposition Using Point Cloud Representation
• Authors: Xiaoyan Xing, Konrad Groh, Sezer Karaoglu, Theo Gevers,
• Abstract summary: We introduce Point Intrinsic Net (PoInt-Net), which leverages 3D point cloud data to concurrently estimate albedo and shading maps. PoInt-Net is efficient, achieving consistent performance across point clouds of any size with training only required on small-scale point clouds.
• Score: 13.771632868567277
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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