Depth completion is the task of recovering dense depth map from sparse ones,usually with the help of color images.Existing image guided methods perform well on daytime depth perception self-driving benchmarks,but stru...Depth completion is the task of recovering dense depth map from sparse ones,usually with the help of color images.Existing image guided methods perform well on daytime depth perception self-driving benchmarks,but struggle in nighttime scenarios with poor visibility and complex illumination.To address these challenges,we propose a simple yet effective learnable differencing center network(LDCNet).The key idea is to use recurrent inter-convolution differencing(RICD)and illumination affinitive intra-convolution differencing(IAICD)to enhance the nighttime color images and reduce the negative effects of the varying illumination,respectively.RICD explicitly estimates global illumination by differencing two convolutions with different kernels,treating the small-kernel-convolution feature as the center of the large-kernel-convolution feature in a new perspective.IAICD softly alleviates the local relative light intensity by differencing a single convolution,where the center is dynamically aggregated based on neighboring pixels and the estimated illumination map in the RICD.On both nighttime depth completion and depth estimation tasks,extensive experiments demonstrate the effectiveness of our LDCNet,reaching the state of the art.展开更多
基金supported by the National Science Fund of China(No.62072242 and No.62361166670)the Young Scientists Fund of the National Natural Science Foundation of China(No.62206134)+1 种基金the Fundamental Research Funds for the Central Universities(No.070-63233084)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX23_0471).
文摘Depth completion is the task of recovering dense depth map from sparse ones,usually with the help of color images.Existing image guided methods perform well on daytime depth perception self-driving benchmarks,but struggle in nighttime scenarios with poor visibility and complex illumination.To address these challenges,we propose a simple yet effective learnable differencing center network(LDCNet).The key idea is to use recurrent inter-convolution differencing(RICD)and illumination affinitive intra-convolution differencing(IAICD)to enhance the nighttime color images and reduce the negative effects of the varying illumination,respectively.RICD explicitly estimates global illumination by differencing two convolutions with different kernels,treating the small-kernel-convolution feature as the center of the large-kernel-convolution feature in a new perspective.IAICD softly alleviates the local relative light intensity by differencing a single convolution,where the center is dynamically aggregated based on neighboring pixels and the estimated illumination map in the RICD.On both nighttime depth completion and depth estimation tasks,extensive experiments demonstrate the effectiveness of our LDCNet,reaching the state of the art.
