🤖 AI Summary
This work addresses the limitation of local convolutions in monocular depth estimation, which struggle to capture long-range spatial dependencies. To overcome this, the authors propose GraphDepth, a novel architecture that integrates GraphSAGE graph neural networks into the multi-scale feature layers of a ResNet-101 U-Net to explicitly model global spatial relationships. Key innovations include a scalable batch-parallel graph construction strategy, multi-scale GNN integration, channel-attention-gated skip connections, and heteroscedastic uncertainty estimation. The method achieves near state-of-the-art accuracy on NYU Depth V2 at 25 FPS with only 3.8 GB GPU memory and sets a new best RMSE of 8.24 m on the WHU Aerial dataset, demonstrating exceptional cross-domain generalization capability.
📝 Abstract
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
grid-based adjacency for scalable training; (2) multi-scale GraphSAGE integration at bottleneck and decoder stages
(1/32, 1/16, 1/8 resolution) to propagate global context
throughout the feature hierarchy; (3) channel-attention gated skip connections that adaptively weight encoder
features before fusion; and (4) heteroscedastic uncertainty estimation via a dedicated aleatoric
uncertainty head, enabling confidence-aware loss weighting during
optimization. Unlike transformer-based hybrids, which suffer from quadratic complexity in sequence length, GraphDepth scales linearly with spatial resolution while achieving comparable global receptive fields through iterative message passing. Experiments on NYU Depth V2, WHU Aerial, ETH3D, and Mid-Air benchmarks demonstrate competitive accuracy within 4.6\% of state-of-the-art transformers on indoor scenes with substantially lower computational cost (25 FPS vs 9 FPS, 3.8 GB vs 8.8 GB VRAM). GraphDepth achieves the best reported result on WHU Aerial (RMSE 8.24 m) and exhibits superior zero-shot cross-domain transfer to the Mid-Air synthetic aerial dataset, validating the generalization power of explicit relational reasoning for depth estimation.