.png){kind=icon}
Return
Attention-based context aggregation network for monocular depth estimation
.png){kind=icon}
OA
AI
DOI：10.1007/s13042-020-01251-y.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Depth estimation is a traditional computer vision task, which plays a crucial role in understanding 3D scene geometry. Recently, algorithms that combine the multi-scale features extracted by the dilated convolution based block (atrous spatial pyramid pooling, ASPP) have gained significant improvements in depth estimation. However, the discretized and predefined dilation kernels cannot capture the continuous context information that differs in diverse scenes and easily introduce the grid artifacts. This paper proposes a novel algorithm, called attention-based context aggregation network (ACAN) for depth estimation. A supervised self-attention model is designed and utilized to adaptively learn the task-specific similarities between different pixels to model the continuous context information. Moreover, a soft ordinal inference is proposed to transform the predicted probabilities to continuous depth values which reduce the discretization error (about 1% decrease in RMSE). ACAN achieves state-of-the-art performance on public monocular depth-estimation benchmark datasets. The source code of ACAN can be found in .
Keywords:
Depth estimation
Attention model
Context aggregation
Convolutional neural networks
Deep learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.7
Papers:
3.2K
Citations:
5.6K
Organization
No organization information available
Cited Papers
Carboxylated pillar[n]arene (n= 5–7) host molecules: high affinity and selective binding in water
.png){kind=icon}
PREAI
ShareSaveSegFast-V2: Semantic image segmentation with less parameters in deep learning for autonomous driving
PREAI
ShareSaveShareSave
ShareSaveShareSaveShareSave
View more