Complete 3D scene by fusion of depth and color features at different scales with AGG-Net
Complete 3D scene by fusion of depth and color features at different scales with AGG-Net
AGG-Net: Attention Guided Gated-convolutional Network for Depth Image Completion
arXiv paper abstract https://arxiv.org/abs/2309.01624
arXiv PDF paper https://arxiv.org/pdf/2309.01624.pdf
... RGB-D cameras based on TOF, structured light, or binocular vision acquire some invalid data inevitably, such as weak reflection, boundary shadows, and artifacts
... propose a new model for depth image completion based on the Attention Guided Gated-convolutional Network (AGG-Net), through which more accurate and reliable depth images can be obtained from the raw depth maps and the corresponding RGB images.
... model employs a UNet-like architecture which consists of two parallel branches of depth and color features.
In the encoding stage, an Attention Guided Gated-Convolution (AG-GConv) module is proposed to realize the fusion of depth and color features at different scales, which can effectively reduce the negative impacts of invalid depth data on the reconstruction.
In the decoding stage, an Attention Guided Skip Connection (AG-SC) module is presented to avoid introducing too many depth-irrelevant features to the reconstruction.
... method outperforms the state-of-the-art methods on the popular benchmarks NYU-Depth V2, DIML, and SUN RGB-D.
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