EfficientPENet: Real-Time Depth Completion from Sparse LiDAR via Lightweight Multi-Modal Fusion
This work addresses the challenge of efficient depth completion from sparse LiDAR point clouds and RGB images by proposing a lightweight dual-branch network. Built upon ConvNeXt as the backbone, the method integrates sparsity-invariant convolutions with a Convolutional Spatial Propagation Network (CSPN), employing late fusion and multi-scale depth supervision. It is the first to introduce layer normalization, large-kernel depthwise convolutions, and stochastic depth regularization into depth completion. Additionally, a position-aware test-time augmentation strategy is devised. On the KITTI benchmark, the model achieves an RMSE of 631.94 mm with only 36.24M parameters and a latency of 20.51 ms (48.76 FPS), reducing the parameter count by 3.7× and accelerating inference by 23× compared to BP-Net.