AI RESEARCH
Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation
arXiv CS.CV
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ArXi:2605.10251v1 Announce Type: new We present GraphDepth, a monocular depth estimation architecture that synergistically integrates Graph Neural Networks (GNNs) within a convolutional encoder-decoder framework. Our approach embeds efficient GraphSAGE layers at multiple scales of a ResNet-101 U-Net backbone, enabling explicit modeling of long-range spatial relationships that lie beyond the receptive field of local convolutions. Key technical contributions include: (1) batch-parallelized graph construction with configurable k-NN and.