.png){kind=icon}
.png){kind=icon}
Return
A simple monocular depth estimation network for balancing complexity and accuracy
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-025-97568-1.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Monocular depth estimation plays a crucial role in many downstream visual tasks. Although research on monocular depth estimation is relatively mature, it commonly involves strategies that entail increasing both the computational complexity and the number of parameters to achieve superior performance. Particularly in practical applications, enhancing the accuracy of depth prediction while ensuring computational efficiency remains a challenging issue. To tackle this challenge, we propose a novel and simple depth estimation model called SimMDE, which treats monocular depth estimation as an ordinal regression problem. Beginning with a baseline encoder, our model is equipped with a Deformable Cross-Attention Feature Fusion (DCF) decoder with sparse attention. This decoder efficiently integrates multi-scale feature maps, markedly reducing the quadratic complexity of the Transformer model. For the extraction of finer local features, we propose a Local Multi-dimensional Convolutional Attention (LMC) module. Meanwhile, we propose a Wavelet Attention Transformer (WAT) module to achieve pixel-level precise classification of images. Furthermore, we also conduct extensive experiments on two widely recognized depth estimation benchmark datasets: NYU and KITTI. The experimental findings unequivocally demonstrate that our model attains exceptional accuracy in depth estimation while upholding high computational efficiency. Remarkably, our framework SimMDE, extending from AdaBins, demonstrates enhancements, resulting in substantial improvements of 11.7% and 10.3% in the absolute relative error (AbsRel) on the NYU and KITTI datasets, respectively, with fewer parameters.
Keywords:
Monocular depth estimation
Deformable cross-attention
Transformer
Adaptive bins
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.9
Papers:
27.4W
Citations:
83.5W