With the wide application of sparse ToF sensors in mobile devices, RGB
image-guided sparse depth completion has attracted extensive attention
recently, but still faces some problems. First, the fusion of multimodal
information requires more network modules to process different modalities. But
the application scenarios of sparse ToF measurements usually demand lightweight
structure and low computational cost. Second, fusing sparse and noisy depth
data with dense pixel-wise RGB data may introduce artifacts. In this paper, a
light but efficient depth completion network is proposed, which consists of a
two-branch global and local depth prediction module and a funnel convolutional
spatial propagation network. The two-branch structure extracts and fuses
cross-modal features with lightweight backbones. The improved spatial
propagation module can refine the completed depth map gradually. Furthermore,
corrected gradient loss is presented for the depth completion problem.
Experimental results demonstrate the proposed method can outperform some
state-of-the-art methods with a lightweight architecture. The proposed method
also wins the championship in the MIPI2022 RGB+TOF depth completion challenge.

本文提出了一种轻量的深度完成网络，包括两个分支全局和局部深度预测模块和漏斗卷积空间传播网络，通过轻量级骨干提取和融合交叉模式特征，改进了空间传播模块可以逐步改善修饰后的深度图，解决了 RGB 图像引导的稀疏深度完成问题，通过修正的梯度损失解决了深度完成问题，并在 MIPI2022 RGB + TOF 深度完成挑战中获得了冠军。