RENA-Depth: toward recursion representation enhancement in neighborhood attention guided lightweight self-supervised monocular depth estimation

Although self-supervised depth estimation models based on transformers have achieved success, lightweight depth prediction networks exhibit a particularly pronounced issue with depth prediction blurriness at object boundaries compared to standard depth prediction networks. We found that this problem arises from the token dimension constraints, which limit the precise representation of semantic and spatial information. To address this challenge, we introduce a lightweight monocular self-supervised depth estimation network, RENA-Depth, which leverages convolutional neural network neighborhood attention-guided recursive Transformers to enhance depth estimation precision. Specifically, the design begins with the introduction of neighborhood adaptive attention (NA), which focuses on local and regional scales. This component adaptively mines latent semantic and spatial information from the neighborhoods of the input features of self-attention. Subsequently, a global feature recursive interaction module was developed to recursively refine the interaction between local and global information, enhancing the representation of semantic and spatial information without a significant increase in parameters. Finally, an attention equilibrium loss is proposed, which motivates richer semantic information representation and clarifies boundary depth by penalizing the orthogonality similarity of attention mechanisms. Extensive evaluations on the Karlsruhe Institute of Technology and Toyota Technological Institute and Make3D datasets have demonstrated that the proposed lightweight self-supervised depth estimation model, RENA-Depth, outperforms the most advanced lightweight depth detection algorithms, confirming its efficacy and innovation in improving depth prediction accuracy.