A Novel Lightweight Network for Fast Monocular Depth Estimation

Tim Heydrich, Yimin Yang, Xiangyu Ma, Yu Liu, Shan Du

Abstract: Depth estimation is of growing interest in many sectors, from robotics to wearable augmented reality gears. Monocular depth estimation attracts more attention due to its cost efficiency and low complexity. Most recent research has developed very large and resource intensive networks which are not suitable for small systems with limited resources. In this paper, we propose a lightweight network which leverages the advantages of dimension-wise convolutions and depthwise separable convolutions to reduce complexity in the architecture. In particular, the proposed depth estimation architecture utilizes a novel DICE unit-based encoder, optimized for a lightweight encoder-decoder structure. Furthermore, we propose a DICE unit-based decoder structure as well as an optimized depthwise separable convolution-based decoder. Both decoders follow a similar five-layer architecture. In the experiments, we have demonstrated the effectiveness of the proposed architecture as well as the comparison between the two proposed decoders. Our novel lightweight network has a significant decrease in both size and complexity at a marginal cost to accuracy when compared to other state-of-the-art lightweight networks.