DySPN: Learning Dynamic Affinity for Image-Guided Depth Completion

Image-guided depth completion (IGDC) is a multimodal computer vision task for acquiring high-precision dense depth maps. It reasonably predicts values around accurate sparse depth measurements by relying on the details of simultaneous dense RGB images. To achieve this goal, spatial propagation networks (SPNs) elaborate context-aware meta cells that connect pixels to their neighbors and fuse sparse and dense modalities by linear propagation. However, static affinity matrices and fixed neighborhood connections limit the representation of the networks. In this paper, our proposed dynamic SPN (DySPN) uses a nonlinear propagation model (NLPM), which processes the propagation more finely by adjusting the affinity weights, diffusion paths, and the number of neighbors. Specifically, we first generate adaptive weighting (AW) matrices by decoupling the neighborhood into parts with respect to different distances. Independent attention maps are recursively applied to refine the weight value. Furthermore, a dynamic path (DP) strategy is adopted to unfreeze the links of the neighborhood for learning variable connections. The solution space of the paths is also constrained by a propagation decay loss to keep the results stable. Finally, we introduce a diffusion suppression (DS) operation, which preserves the edge of dense depth maps by manipulating the AW and DP strategies to decelerate and terminate the propagation. In our experiment, the proposed method requires fewer iterations and neighbors than other SPNs while yielding better results. DySPN outperforms state-of-the-art (SoTA) methods on the KITTI DC, NYU Depth v2, and VOID datasets. Our code is available at:
https://github.com/Kyakaka/DySPN
.