提升低成本深度传感器的补全效果，解决视野外区域精度差问题
CFPNet: Improving Lightweight ToF Depth Completion via Cross-zone Feature Propagation
- 设计跨区域特征传播模块，从有深度信号区向无信号区传递信息
- 在ZJU-L5数据集上达到当前最佳性能，显著改善视野外区域精度
- 适合做轻量级深度感知应用的开发者参考
使用低成本的轻量级飞行时间（ToF）深度传感器进行深度补全是极具吸引力的方向。然而，与摄像头相比，轻量级ToF传感器通常具有有限的视场（FOV），因此只有图像中特定区域（即“区”）的像素能获得深度信号。先前方法无法有效将该区域的深度特征传播到外部区域，导致外部区域的深度补全性能下降。为此，本文提出CFPNet，通过两个新模块实现从区内到区外的跨区域特征传播：第一个是基于直接注意力的传播模块（DAPM），强制实现跨区域特征获取；第二个是大核卷积传播模块（LKPM），利用最大为31的卷积核实现跨区域特征传播。通过合理结合这两个模块，CFPNet在ZJU-L5数据集上的大量实验验证中实现了当前最优（SOTA）的深度补全性能。代码已公开于https://github.com/denyingmxd/CFPNet。
原文摘要 · Abstract (English)
Depth completion using lightweight time-of-flight (ToF) depth sensors is attractive due to their low cost. However, lightweight ToF sensors usually have a limited field of view (FOV) compared with cameras. Thus, only pixels in the zone area of the image can be associated with depth signals. Previous methods fail to propagate depth features from the zone area to the outside-zone area effectively, thus suffering from degraded depth completion performance outside the zone. To this end, this paper proposes the CFPNet to achieve cross-zone feature propagation from the zone area to the outside-zone area with two novel modules. The first is a direct-attention-based propagation module (DAPM), which enforces direct cross-zone feature acquisition. The second is a large-kernel-based propagation module (LKPM), which realizes cross-zone feature propagation by utilizing convolution layers with kernel sizes up to 31. CFPNet achieves state-of-the-art (SOTA) depth completion performance by combining these two modules properly, as verified by extensive experimental results on the ZJU-L5 dataset. The code is available at https://github.com/denyingmxd/CFPNet.
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