CFPNet: Improving Lightweight ToF Depth Completion via Cross-zone Feature Propagation
This addresses depth completion for low-cost ToF sensors, but it appears incremental as it builds on prior methods by improving feature propagation.
The paper tackles the problem of depth completion for lightweight time-of-flight sensors, which have a limited field of view, by proposing CFPNet to propagate depth features from zone to outside-zone areas, achieving state-of-the-art performance on the ZJU-L5 dataset.
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.