Compact Depth-Wise Separable Precise Network for Depth Completion

Abstract

Predicting a dense depth map from synchronized LiDAR scans and RGB images using compact deep neural networks presents a significant challenge. While most state-of-the-art models enhance prediction accuracy by increasing the number of parameters, leading to substantial memory consumption, depth completion tasks in areas such as autonomous driving primarily utilize edge devices powered by embedded GPUs. In this paper, we introduce a methodology for creating an efficient, high-fidelity depth completion model derived from a base model. Our proposed compact model replaces conventional convolutional encoder layers with depth-wise separable convolutions, and transposed convolutional decoders with up-sampling plus depth-wise separable convolution. We further employ random layer pruning as a stability test, guiding the design of our architecture and preventing over-parameterization. Additionally, we introduce a straightforward yet robust knowledge distillation method to enhance network performance and improve model scalability to meet higher quality requirements. Our experimental results demonstrate substantial improvement over existing compact models in terms of state-of-the-art performance, while significantly reducing the number of parameters compared to larger models.

Keywords

• CNN
• computer-vision
• autonomous driving
• depth completion
• model compression
• knowledge distillation