CHADET: Cross-Hierarchical-Attention for Depth-Completion Using Unsupervised Lightweight Transformer

We propose CHADET, an unsupervised lightweight depth completion network that combines RGB and sparse depth via depthwise encoders and a cross-hierarchical-attention transformer decoder, producing sharper object boundaries while reducing memory and enabling real-time inference.