# S&CNet: Monocular Depth Completion for Autonomous Systems and 3D Reconstruction

## TL;DR

This paper introduces S&CNet, a lightweight and efficient monocular depth completion network that balances accuracy and speed, utilizing a dual-stream attention module to enhance feature relationships, achieving competitive results on KITTI dataset.

## Contribution

The paper proposes S&CNet with a novel dual-stream attention module, improving depth completion accuracy while maintaining high efficiency and speed.

## Key findings

- Achieves nearly four times faster performance than existing methods.
- Maintains competitive accuracy on KITTI dataset.
- Enhances other models' performance with minimal additional cost.

## Abstract

Dense depth completion is essential for autonomous systems and 3D reconstruction. In this paper, a lightweight yet efficient network (S\&CNet) is proposed to obtain a good trade-off between efficiency and accuracy for the dense depth completion. A dual-stream attention module (S\&C enhancer) is introduced to measure both spatial-wise and the channel-wise global-range relationship of extracted features so as to improve the performance. A coarse-to-fine network is designed and the proposed S\&C enhancer is plugged into the coarse estimation network between its encoder and decoder network. Experimental results demonstrate that our approach achieves competitive performance with existing works on KITTI dataset but almost four times faster. The proposed S\&C enhancer can be plugged into other existing works and boost their performance significantly with a negligible additional computational cost.