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Depth-Centric Dehazing and Depth-Estimation from Real-World Hazy Driving Video
Junkai Fan, Kun Wang, Zhiqiang Yan +4
In this paper, we study the challenging problem of simultaneously removing haze and estimating depth from real monocular hazy videos. These tasks are inherently complementary: enha…
Completion as Enhancement: A Degradation-Aware Selective Image Guided Network for Depth Completion
Zhiqiang Yan, Zhengxue Wang, Kun Wang +2
In this paper, we introduce the Selective Image Guided Network (SigNet), a novel degradation-aware framework that transforms depth completion into depth enhancement for the first t…
DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine Domain
Kun Wang, Zhiqiang Yan, Junkai Fan +4
In this paper, we introduce DCDepth, a novel framework for the long-standing monocular depth estimation task. Moving beyond conventional pixel-wise depth estimation in the spatial…
Driving-Video Dehazing with Non-Aligned Regularization for Safety Assistance
Junkai Fan, Jiangwei Weng, Kun Wang +4
Real driving-video dehazing poses a significant challenge due to the inherent difficulty in acquiring precisely aligned hazy/clear video pairs for effective model training, especia…