Unsupervised Intrinsic Image Decomposition with LiDAR Intensity

Sato, Shogo; Yao, Yasuhiro; Yoshida, Taiga; Kaneko, Takuhiro; Ando, Shingo; Shimamura, Jun

doi:10.1109/cvpr52729.2023.01294

Cited by 4 publications

(1 citation statement)

References 34 publications

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“…Deep learning and neural networks have developed vigorously in recent years [16,17]. In this field of IID, there are two main methods: unsupervised learning [18,19] and supervised learning [20][21][22][23][24]. Unsupervised learning does not rely on labeled training data.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Intrinsic Image Decomposition with Transformer and Laplacian Pyramid Network

Liu,

Ma,

Meng

et al. 2024

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Intrinsic Image Decomposition (IID) remains a pivotal challenge in the domain of computer vision, with applications spanning image editing, color image denoising, and segmentation, among others. Despite notable successes, there exists a significant opportunity for enhancing the feature encoding process to improve the accuracy of predicted outcomes. In response to this, a novel framework, termed Transformer and Laplacian Pyramid Network (TLPNet), is introduced. TLPNet comprises two distinct sub-networks: the Transformer for Reflectance Network (TRNet) and the Laplacian Pyramid for Shading Network (LPSNet). Within this framework, the Transformer module is strategically employed within the reflectance imaging component to effectively address the challenge of inadequate feature information extraction. Comprehensive experiments conducted on the ShapeNet Dataset and MIT Dataset have demonstrated the efficacy of TLPNet in predicting more accurate reflectance and shading images. This study contributes to the field by presenting an innovative approach that leverages the strengths of transformer models and Laplacian pyramid structures for the task of IID, setting a new benchmark for future research in the area.

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