Adaptive Fusion of RGB Image Features for Sparse Depth Completion
The purpose of depth completion is to restore dense depth images from sparse depth images.Existing methods usually take sparse depth images and their corresponding RGB images as input and restore dense depth images through a convolutional neural network.However,ordinary convolutional layers have large limitations in dealing with sparse and ir-regular depth information,while RGB image features and depth image features belong to different modalities.To address these problems,an adaptive sparse invariant module to handle sparse depths according to the validity of the input pixels is proposed.The multi-scale features fusion incorporating attention mechanism is also proposed to further improve the depth completion performance by suppressing unnecessary features while focusing on effective features.A series of experiments are conducted on the NYUv2 dataset,and the experimental results demonstrate the effectiveness of the proposed algo-rithm and module.
国家科技期刊平台
NSTL
万方数据
维普
